We propose a novel walking control scheme based on the dynamics of the Linear Inverted Pendulum (LIP) model. The pattern generation incorporates a model of contact forces, enabling closed-loop control of the humanoid robot's state, including the Center of Mass (CoM) position, velocity, and Zero Moment Point (ZMP). No additional control policies are required to maintain static and dynamic balance. Our approach also includes dynamic re-planning of step locations and timings, thus preserving the LIP's boundedness condition. We validated this controller on five different humanoid robots, testing its robustness through various disturbances, including sudden pushes during walking and static phases. Additionally, our controller demonstrated effective locomotion over uneven and compliant terrain. Both simulation and experimental results confirm the effectiveness and robustness of this controller.

We present a lightweight, real-time gesture responsive framework designed to investigate dynamics of human-robot interaction in live dance performances, merging aspects from choreography and robotics. In particular, it is tailored for artists to integrate human-sized humanoid robots into their dances seamlessly, offering an intuitive solution without the complexities of mastering robot control systems. Unlike existing interaction methods relying on wearable sensors or predetermined music cues, our framework, integrated in a task-space controller, enables the robot to dynamically respond to the dancer’s movements, generating unpredictable yet artistically meaningful gestures without the need for body-mounted sensors. This design choice emphasizes the artistic intention behind the improvisation, illustrating that it is not merely about creating movements spontaneously but rather a way of expression in intuition guided by perception. We assess our framework on the HRP-4 humanoid robot and the result has been successfully demonstrated in a public human-robot dance performance at the Arts Center of Enghien-les-Bains’s 23/24 season launch event, aiming to contribute to the exploration of co-creation between human and human-sized humanoid robot in improvised dance performances.

To efficiently achieve complex humanoid loco-manipulation tasks in industrial contexts, we propose a combined vision-based tracker-localization interplay integrated as part of a task-space whole-body optimization control. To achieve good perception complementarity between manipulation and localization, a new fast dense 3D model-based tracking using wide-angle depth image is developed and used in conjunction with a simultaneous localization and mapping software. Our approach allows humanoid robots, targeted for industrial manufacturing, to manipulate and assemble large-scale objects while walking. It is assessed with experiments consisting in rolling and assembling in an unwinder a heavy and wide bobbin using bimanual grasping and bipedal locomotion at a time. This experimental use-case is found in some large-scale manufacturing where bobbins are enrolled with various materials (cables, papers, rubbers, etc.). The same experiments are made using two different humanoid robots of the same family.Note to Practitioners—This paper aims at deploying humanoid robots in large-scale manufacturing industries. We consider non-added value tasks related to transporting large tools or objects such as large bobbins by means of locomanipulation skills, similarly to human workers. We developed a task-space control framework that has been successfully applied in the aircraft industry. In the frame of a current collaboration with other major industrial sectors, we enhanced our control framework to interplay between SLAM and visual tracking to realize robust loco-manipulation tasks. Our approach can be applied and ported to any humanoid robot or bi-manual wheeled mobile robots with minor programming effort as the software is made open. Preliminary experiments with two different humanoids and use-cases suggest that our approach is feasible. In future research, we will address the problem of performance to reach at least human-speed in the execution of locomanipulation tasks in large-scale industry and automation contexts.

A robot can affect its social environment beyond the person who is interacting with it. Within this context, we believe it is important to explore Human-Robot Interactions (HRI) in complex social settings. We examine the effect of different robot shapes in a multi-person context during dance routines and observe how the design of the robot enhances the artistic process. We identify key factors through which human preferences are being shaped, within a novel third party setting human-robot-human interaction (HRHI).

Task-space quadratic programming (QP) is an elegant approach for controlling robots subject to constraints. Yet, in the case of kinematic-controlled (i.e., high-gain position or velocity) robots, the closed-loop QP control scheme can be prone to instability depending on how the gains related to the tasks or the constraints are chosen. In this article, we address such instability shortcomings. First, we highlight the nonrobustness of the closed-loop system against nonmodeled dynamics, such as those relative to joint dynamics, flexibilities, external perturbations, etc. Then, we propose a robust QP control formulation based on high-level integral feedback terms in the task space including the constraints. The proposed method is formally proved to ensure closed-loop robust stability and is intended to be applied to any kinematic-controlled robots under practical assumptions. We assess our approach through experiments on a fixed-base robot performing stable fast motions and a floating-base humanoid robot robustly reacting to perturbations to keep its balance.

We discuss a novel method for estimating task Cartesian position and velocity in robot manipulators. This is done by model-based fusion of inertial measurement units with motor encoders. The model is developed to robustly handle the uncertainties in the trajectory. Thus, not only the approach benefits from high fidelity and bandwidth thanks to multiple-sensory fusion, but it also enforces stability despite poorly formulated motions. This empowers the method to be utilized in complex closed-loop applications, where both task position and velocity information is required.

Bidirectional object handover between a human and a robot enables an important functionality skill in robotic human-centered manufacturing or services. The problem in achieving this skill lies in the capacity of any solution to deal with three important aspects: (i) synchronized timing for the handing over phases; (ii) the handling of object pose constraints; and (iii) understanding the haptic exchanging to seamlessly achieve some steps of the (i). We propose a new approach for (i) and (ii) consisting in explicitly formulating the handover process as constraints in a task-space quadratic programming control framework to achieve implicit time and trajectory encounters. Our method is implemented on Panda robotic arm taking objects from a human operator.

Rather than systematically programming joint or task trajectories, having a human physically manipulate the robot for direct adjustments is more intuitive, saves time, and increases usability, especially for nonexperts. Interactive motion generation or repositioning of humanoid robots through direct human-touch manipulation is not an easy task, especially for high-level multijoint maneuvers. We propose a set of design rules for generating intuitive touch semantics called the “two-touch kinematic chain paradigm.” Our method interprets user touch intentions to allow motions ranging from low-level single joint control to high-level whole-body task control with posture generation, stepping, and walking. The goal is to provide the user with an intuitive protocol for physical humanoid manipulation that can serve the purpose of any application. The generated set of touch semantics is embodied in a finite state machine-based framework using a task-space quadratic programming controller to interpret human touch using capacitive sensors embedded in the humanoid shell, and force-torque sensors located at the ankles and wrists. A position-controlled humanoid robot is used to assess the utility and function of our proposed touch semantics for physical manipulation. Furthermore, a user study with nonexperts examines how our approach is perceived in practice.

Safely generating impacts with robots is challenging due to subsequent discontinuous velocity and high impact forces. We aim at increasing the impact velocity – the robot’s relative speed prior to contact – such that impact-tasks like grabbing and boxing are made with the highest allowable speed performance when needed. Previous works addressed this problem for rigid bodies’ impacts. This letter proposes a control paradigm for generating intentional impacts with deformable contacts that incorporates hardware and task constraints. Based on data-driven learning of the shock-absorbing soft dynamics and a novel mapping of joint-space limits to contact-space, we devise a constrained model-predictive control to maximize the intentional impact within a feasible, robot-safe level. Our approach is assessed with real-robot experiments on the redundant Panda manipulator, demonstrating high pre-impact velocities (up to 0.9 m/s) of a rigid end-effector on soft objects and an end-effector soft suction-pump on rigid or deformable objects.

In this paper we benchmark different types of cameras and evaluate their performance in terms of reliable localization reliability and precision in Visual Simultaneous Localization and Mapping (vSLAM). Such benchmarking is merely found for visual odometry, but never for vSLAM. Existing studies usually compare several algorithms for a given camera. The evaluation methodology we propose is applied to the recent OpenVSLAM framework. The latter is versatile enough to natively deal with perspective, fisheye, 360 cameras in a monocular or stereoscopic setup, an in RGB or RGB-D modalities. Results in various sequences containing light variation and scenery modifications in the scene assess quantitatively the maximum localization rate for 360 vision. In the contrary, RGB-D vision shows the lowest localization rate, but highest precision when localization is possible. Stereo-fisheye trades-off with localization rates and precision between 360 vision and RGB-D vision. The dataset with ground truth will be made available in open access to allow evaluating other/future vSLAM algorithms with respect to these camera types.

This study deals with the balance of humanoid or multi-legged robots in a multi-contact setting where a chosen subset of contacts is undergoing desired sliding-task motions. One method to keep balance is to hold the center-of-mass (CoM) within an admissible convex area. This area is calculated based on the contact positions and forces. We introduce a methodology to compute this CoM support area (CSA) for multiple fixed and intentionally sliding contacts. To select the most appropriate CoM position within CSA, we account for (i) constraints of multiple fixed and sliding contacts, (ii) desired wrench distribution for contacts, and (iii) desired CoM position (eventually dictated by other tasks). These are formulated as a quadratic programming (QP) optimization problems. We illustrate our approach with pushing against a wall and wiping, and conducted experiments using the HRP-4 humanoid robot.

We study the use of humanoid robot technology for physical assistance in motion for a frail person. A careful design of a whole-body controller for a humanoid robot needs to be developed in order to ensure efficient, intuitive and secure interaction between humanoid-assistant and human-patient. Here, we present a design and implementation of a whole-body controller that enables a humanoid robot with a mobile base to autonomously reach a person, perform audiovisual communication of intent, and establish several physical contacts for initiating physical assistance. Our controller uses (i) visual human perception as a feedback for navigation and (ii) joint residual signal based contact detection for closed-loop physical contact creation. We assess the developed controller on a healthy subject and report on the experiments achieved and the results.

We consider dynamic stair climbing with the HRP-4 humanoid robot as part of an Airbus manufacturing use-case demonstrator. We share experimental knowledge gathered so as to achieve this task, which HRP-4 had never been challenged to before. In particular, we extend walking stabilization based on linear inverted pendulum tracking [1] by quadratic programming-based wrench distribution and a whole-body admittance controller that applies both end-effector and CoM strategies. While existing stabilizers tend to use either one or the other, our experience suggests that the combination of these two approaches improves tracking performance. We demonstrate this solution in an on-site experiment where HRP4 climbs an industrial staircase with 18.5 cm high steps, and release our walking controller as open source software.

We propose a novel method adaptive subdivision (AS) to evaluate the distance function for moving general polygonal models. The distance function can have a positive and a negative value, each of which corresponds to the Euclidean distance and penetration depth, respectively. In our approach, the distance between a pair of objects can be evaluated along any time interval of the object's trajectory; therefore it is called “continuous”, and a minimum of the continuous distance (MCD) is determined for collision avoidance. In order to compute a MCD for general polygonal models, we calculate the upper and lower bounds of the distance in the time interval and abandons the time intervals that cannot realize the MCD. We have implemented our distance evaluation method, and have experimentally validated the proposed methods to effectively and accurately find the MCDs to generate a collision-free motion for the HRP-2 humanoid robot.

Walking gaits generated using Model Predictive Control (MPC) is widely used due to its capability to handle several constraints that characterize humanoid locomotion. The use of simplified models such as the Linear Inverted Pendulum allows to perform computations in real-time, giving the robot the fundamental capacity to replan its motion to follow external inputs (e.g. reference velocity, footstep plans). However, usually the MPC does not take into account the current state of the robot when computing the reference motion, losing the ability to react to external disturbances. In this paper a closed-loop MPC scheme is proposed to estimate the robot's real state through Simultaneous Localization and Mapping (SLAM) and proprioceptive sensors (force/torque). With the proposed control scheme it is shown that the robot is able to react to external disturbances (push), by stepping to recover from the loss of balance. Moreover the localization allows the robot to navigate to target positions in the environment without being affected by the drift generated by imperfect open-loop control execution. We validate the proposed scheme through two different experiments with a HRP-4 humanoid robot.

This paper contributes to the field of physical human-robot collaboration. We present a complete control framework, which aims at making humanoid robots capable of carrying objects together with humans. First, we design a template identifying the primitive subtasks necessary for collaborative carrying. Then, these subtasks are formulated as constrained optimization problems for controlling the whole-body motion of a humanoid robot. The subtasks include two walking pattern generators that account for physical collaboration, as well as posture and grasping controllers. Finally, we validate our framework in a variety of collaborative carrying experiments, using the HRP-4 humanoid robot.

We have extended the task-space multiobjective controllers that write as quadratic programs (QPs) to handle multirobot systems as a single centralized control. The idea is to assemble all the “robots” models and their interaction task constraints into a single QP formulation. By multirobot, we mean that whatever entities a given robot will interact with (solid or articulated systems, actuated, partially or not at all, fixed-base or floating-base), we model them as clusters of robots and the controller computes the state of each cluster as an overall system and their interaction forces in a physically consistent way. By doing this, the tasks specification simplifies substantially. At the heart of the interactions between the systems are the contact forces; methodologies are provided to achieve reliable force tracking by our multirobot QP controller. The approach is assessed by a large panel of experiments on real complex robotic platforms (full-size humanoid, dexterous robotic hand, fixed-base anthropomorphic arm) performing whole-body manipulations, dexterous manipulations, and robot-robot comanipulations of rigid floating objects and articulated mechanisms, such as doors, drawers, boxes, or even smaller mechanisms like a spring-loaded click pen.

The dynamics of a humanoid robot cannot be correctly described independently from the external forces acting on it. These forces have to be reconstructed to enable the robot to control them or to compensate for them. Force sensors are usually used to measure these forces, but because of their cost, they are often put only on the ankle/feet and possibly the wrists. This paper addresses the issue of the estimation of external forces and moments that apply at any part of a robot without direct force measurements and without torque measurements. The sensors used are the regular force sensors and the IMUs of the robot. The method relies on a model-based estimator able to make the fusion between these sensors and the whole body dynamics. The estimator reconstructs a single state vector containing the floating-base kinematics, a filtered measurement of contact force and an additional estimation external force that we evaluate in this paper. Validation is performed on HRP-2 in a multi-contact motion.

We propose a control framework for torque controlled humanoid robots that efficiently minimizes the tracking error in a Quadratic Programming (QP)formulated as multiobjective weighted tasks with constraints. It results in an optimal dynamically-feasible reference that can be tracked robustly, with exponential convergence, without joint torque feedback, in the presence of non modelled torque bias and low-frequency bounded disturbances. This is achieved by introducing integral gains in a Lyapunov-stable torque control, which exploit the passivity properties of the dynamical model of the robot and their effect on the dynamic constraints of the QP solver. The robustness of this framework is demonstrated in simulation by commanding our robot, the HRP-5P, to achieve simultaneously several objectives in the configuration and the Cartesian spaces, in the presence of non-modeled static and kinetic joint friction, as well as an uncertain torque scale.

In order to enable efficient control of a human-humanoid in physical contact settings, a real-time solution for a contact observer is required. We propose a novel approach for proprioceptive sensor based contact sensing suitable for affordable personal robots with no force/torque or electric current sensing. We combine robot model knowledge and the output of acceleration resolved quadratic programming whole-body controller to make a prediction of expected position tracking error for computing our proposed contact observer signal. We demonstrate the efficiency of our approach in the experiments of contact detection and estimation of collision direction and intensity on a real humanoid robot Pepper platform controlled by a task-space multi-objective quadratic programming controller.

Multiple studies have shown that the mere observation of movements by a robot can affect an observing human's movement; effects referred to as motor contagions. However, previous studies have either analyzed motor contagions induced during (which we call on-line contagions), or induced after (off-line contagions) observation of the robot, but never both together. It thus remains unclear whether and how these two contagions differ from each other. Here, in an empirical industrial co-worker setting, we examine the differences in the off-line and on-line contagions induced in participants by the observation of the same movements performed by a human, or a humanoid robot co-worker. We observed that while the off-line contagions predominantly affect the participant's movement velocity, the on-line contagions affect their movement frequency. Furthermore, the off-line contagions were prominent after observing another human, while the on-line contagions were equally strong with either a human or a humanoid coworker. These results suggest that actions by a humanoid robot can induce distinct effects on human behaviors, during and after observation.

We consider the problem of estimating realistic contact forces during manipulation, backed with ground-truth measurements, using vision alone. Interaction forces are usually measured by mounting force transducers onto the manipulated objects or the hands. Those are costly, cumbersome, and alter the objects' physical properties and their perception by the human sense of touch. Our work establishes that interaction forces can be estimated in a cost-effective, reliable, non-intrusive way using vision. This is a complex and challenging problem. Indeed, in multi-contact, a given motion can generally be caused by an infinity of possible force distributions. To alleviate the limitations of traditional models based on inverse optimization, we collect and release the first large-scale dataset on manipulation kinodynamics as 3.2 hours of synchronized force and motion measurements under 193 object-grasp configurations. We learn a mapping between high-level kinematic features based on the equations of motion and the underlying manipulation forces using recurrent neural networks (RNN). The RNN predictions are consistently refined using physics-based optimization through second-order cone programming (SOCP). We show that our method can successfully capture interaction forces compatible with both the observations and the way humans intuitively manipulate objects, using a single RGB-D camera.

We present a novel technique that unobtrusively estimates forces exerted by human participants in multicontact interaction with rigid environments. Our method uses motion capture only, thus circumventing the need to set up cumbersome force transducers at all potential contacts between the human body and the environment. This problem is particularly challenging, as the knowledge of a given motion only characterizes the resultant force, which can generally be caused by an infinity of force distributions over individual contacts. We collect and release a large-scale dataset on how humans instinctively regulate interaction forces on diverse multicontact tasks and motions. The force estimation framework we propose leverages physics-based optimization and neural networks to reconstruct force distributions that are physically realistic and compatible with real interaction force patterns. We show the effectiveness of our approach on various locomotion and multicontact scenarios.

We propose algorithms to compute the three-dimensional (3-D) robust stability region in multicontact. It is well known that the stability region is a product of convex cones and, hence, is a convex polyhedron. Our stability region extends existing recursive two-dimensional (2-D) static stability approaches to 3-D by accounting for possible center-of-mass accelerations. We provide algorithms that construct the region of robust stability in a systematic way. We compare our algorithms and discuss possible computation of intermediary shapes using morphing. Finally, we provide an example of usage in generating robust static postures that can serve the purpose of multicontact planning.

We propose a framework to generate static robot configurations satisfying a set of physical and geometrical constraints. This is done by formulating nonlinear constrained optimization problems over non-Euclidean manifolds and solving them. To do so, we present a new sequential quadratic programming solver working natively on general manifolds and propose an interface to easily formulate the problems, with the tedious and error-prone work automated for the user. We also introduce several new types of constraints for having more complex contacts or working on forces/torques. Our approach allows an elegant mathematical description of the constraints and we exemplify it through formulation and computation examples in complex scenarios with humanoid robots.

We address the problem of humanoid falling with a decoupled strategy consisting of a pre-impact and a postimpact stage. In the pre-impact stage, geometrical reasoning allows the robot to choose appropriate impact points in the surrounding environment and to adopt a posture to reach them while avoiding impact-singularities and preparing for the postimpact. The surrounding environment can be unstructured and may contain cluttered obstacles. The post-impact stage uses a quadratic program controller that adapts on-line the joint proportional-derivative (PD) gains to make the robot compliant-to absorb impact and post-impact dynamics, which lowers possible damage risks. This is done by a new approach incorporating the stiffness and damping gains directly as decision variables in the QP along with the usually-considered variables of joint accelerations and contact forces. Constraints of the QP prevent the motors from reaching their torque limits during the fall. Several experiments on the humanoid robot HRP-4 in a full-dynamics simulator are presented and discussed.

We present a real-time pattern generator for dynamic walking over rough terrains. Our method automatically finds step durations, a critical issue over rough terrains where they depend on terrain topology. To achieve this level of generality, we consider a Floating-base Inverted Pendulum (FIP) model where the center of mass can translate freely and the zero-tilting moment point is allowed to leave the contact surface. This model is equivalent to a linear inverted pendulum with variable center-of-mass height, but its equations of motion remain linear. Our solution then follows three steps: (i) we characterize the FIP contact-stability condition; (ii) we compute feedforward controls by solving a nonlinear optimization over receding-horizon FIP trajectories. Despite running at 30 Hz in a model-predictive fashion, simulations show that the latter is too slow to stabilize dynamic motions. To remedy this, we (iii) linearize FIP feedback control into a constrained linear-quadratic regulator that runs at 300 Hz. We finally demonstrate our solution in simulations with a model of the HRP-4 humanoid robot, including noise and delays over state estimation and foot force control.

We study the HRP-2Kai humanoid robot's ability to conduct the precise industrial task of fastening bolts in aircraft production. Our contribution stands mainly in high integration of different modules within the whole-body Quadratic Programing-based controller that has not been yet confronted to tasks demanding high precision in the execution and tuning. This includes the use of a robust visual servoing algorithm which allows the robot to move autonomously to a desired target and the design of specific tasks and estimators: a learning and admittance control that enables the robot to interact smoothly with its environment, and a fast and safe method to autonomously detect correct tool on nut insertion. We then show that the controller indeed enables our humanoid robot to achieve such a high precision task.

We present a methodology for estimating joints torque due to external forces applied to a robot with large joints backlash and friction. This undesired non-linearity is common in personal robot, due to the use of low cost mechanical components and type of usage. Our method enables contact detection and human-robot physical interaction capabilities without using extra sensors. The effectiveness of our approach is shown with experiments on a Romeo robot arm from SoftBank Robotics.

We extend the quadratic program (QP)-based task-space character control approach-initially intended for individual character animation-to multiple characters interacting among each other or with mobile/articulated elements of the environment. The interactions between the characters can be either physical interactions, such as contacts that can be established or broken at will between them and for which the forces are subjected to Newton's third law, or behavioral interactions, such as collision avoidance and cooperation that naturally emerge to achieve collaborative tasks from high-level specifications. We take a systematic approach integrating all the equations of motions of the characters, objects, and articulated environment parts in a single QP formulation in order to embrace and solve the most general instance of the problem, where independent individual character controllers would fail to account for the inherent coupling of their respective motions through those physical and behavioral interactions. Various types of motions/behaviors are controlled with only the one single formulation that we propose, and some examples of the original motions the framework allows are presented in the accompanying video.

This paper is about the design of humanoid walking pattern generators to be used for physical collaboration. A particular use case is a humanoid robot helping a human to carry large and/or heavy objects. To do this, we construct a reduced model which takes into account physical interaction. This is used in a model predictive control framework to generate separate behaviors for being a follower or a leader. The approach is then validated both on simulation and on the HRP-4 humanoid robot.

In this paper we propose a method to build a smooth and non-singular map from the unit sphere to a Catmull-Clark subdivision surface. We use a tailored ray-casting algorithm to associate a point of the surface to each point of the sphere. This allows us to have smooth approximate representations for a large variety of (possibly non-convex) meshes that we can use to write numerically well-behaved constraints in gradient-based optimization. In particular, we address the writing of contact constraints between a robot and objects of its environment. We first detail our algorithm, taking care of speed and robustness, then show several use-cases for posture generations with a humanoid robot or a hand.

We study the effect of soft (i.e. compliant) soles in humanoid walking. Adding compliance at the foot sole is important for shock absorption during touchdown and to better cast the ground roughness. But, subsequent deformation needs to be considered in the attitude stabilization. We propose a sole deformation estimator used in the controller design, to ensure that the desired ZMP for stability requirement is fulfilled. We performed two comparison experiments on the HRP-4 humanoid robot with/without the estimator. Experiments showed how the estimator enhances the ZMP stabilization during walking and prevents the robot from falling down.

We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfaction of our constraints, we use polynomial approximations over time intervals. Because each constraint and its gradient can be evaluated independently for each time interval, we end up with a highly parallelizable problem that can take advantage of many-core architectures. Classic robotic computations (geometry, kinematics, and dynamics) can also benefit from parallel processors, and we carefully study their implementation in our context. This results in having a full constraint evaluator running on the GPU. We present several optimization examples with a humanoid robot. They reveal substantial improvements in terms of computation performance compared to a parallel CPU version.

We propose a method for estimation of humanoid and human links' inertial parameters. Our approach formulates the problem as a hierarchical quadratic program by exploiting the linear properties of rigid body dynamics with respect to the inertia parameters. In order to assess our algorithm, we conducted experiments with a humanoid robot and a human subject. We compared ground reaction forces and moments estimated from force measurements with those computed using identified inertia parameters and movement information. Our method is able to accurately reconstruct ground reaction forces and force moments. Moreover, our method is able to estimate correctly masses of the robots links and to accurately detect additional masses placed on the human subject during the experiments.

We present a novel, non-intrusive approach for estimating contact forces during hand-object interactions relying solely on visual input provided by a single RGB-D camera. We consider a manipulated object with known geometrical and physical properties. First, we rely on model-based visual tracking to estimate the object's pose together with that of the hand manipulating it throughout the motion. Following this, we compute the object's first and second order kinematics using a new class of numerical differentiation operators. The estimated kinematics is then instantly fed into a second-order cone program that returns a minimal force distribution explaining the observed motion. However, humans typically apply more forces than mechanically required when manipulating objects. Thus, we complete our estimation method by learning these excessive forces and their distribution among the fingers in contact. We provide a full validity analysis of the proposed method by evaluating it based on ground truth data from additional sensors such as accelerometers, gyroscopes and pressure sensors. Experimental results show that force sensing from vision (FSV) is indeed feasible.

This paper presents a framework to embody a user (e.g. disabled persons) into a humanoid robot controlled by means of brain-computer interfaces (BCI). With our framework, the robot can interact with the environment, or assist its user. The low frequency and accuracy of the BCI commands is compensated by vision tools, such as objects recognition and mapping techniques, as well as shared-control approaches. As a result, the proposed framework offers intuitive, safe, and accurate robot navigation towards an object or a person. The generic aspect of the framework is demonstrated by two complex experiments, where the user controls the robot to serve him a drink, and to raise his own arm.

Humanoid robots being underactuated, they need to interact with their environment to move. When optimizing trajectories, the contact forces must therefore be taken into account. In this paper, we first highlight an issue encountered when using parametrized functions in the presence of equality constraints which need to be satisfied continuously over a time interval. We then propose a parametrization of contact forces and a formulation of the constraints which allow to write a tractable optimization program whose solution verifies at any instant all the constraints where those forces appear. We finish by exemplifying the approach with preliminary results obtained on a HRP-2 humanoid robot.

The mass parameters of robots influence performances of model-based control and validation of the simulation results. The mass parameters provided by CAD data are usually rough approximation of the true parameters. Therefore several methods for estimation of those parameters have been proposed. Their precision depends on the used motion, called optimal exciting trajectories. This paper describes a new approach to determine humanoid robot exciting trajectories for mass parameters identification. The method was inspired by the studies done in the field of human mass parameters identification, and it is based on observation of condition numbers of sub-regressor matrices created from the columns of the regressor matrix. The method has been experimentally applied to identify mass parameters of HRP-2 and HRP-4 humanoid robots. The proposed method is able to reconstruct ground reaction forces and force moments more accurately than parameters obtained from CAD data.

We propose a framework for combining vision and haptic information in human-robot joint actions. It consists of a hybrid controller that uses both visual servoing and impedance controllers. This can be applied to tasks that cannot be done with vision or haptic information alone. In this framework, the state of the task can be obtained from visual information while haptic information is crucial for safe physical interaction with the human partner. The approach is validated on the task of jointly carrying a flat surface (e.g. a table) and then preventing an object (e.g. a ball) on top from falling off. The results show that this task can be successfully achieved. Furthermore, the framework presented allows for a more collaborative setup, by imparting task knowledge to the robot as opposed to a passive follower.

Our work builds largely on Nagasaka's stabilizer in multi-contact motion [1]. Using a sequence of contact stances from an offline multi-contact planner, we use first a Model Predictive Controller to generate a dynamic trajectory of the center of mass, then a whole-body closed-loop model-based controller to track it at best. Relatively to Nagasaka's work, we allow frame changes of the preferred force, provide a heuristic to compute the timing of the transition from purely geometrical features and investigate the synchronization problem between the reduced-model preview control and the whole-body controller. Using our framework, we generate a wide range of 3D motions, while accounting for predictable external forces, which includes transporting objects. Simulation scenarios are presented and obtained results are analyzed and discussed.

In this paper we propose a simple way to formulate geometric contact formation to have an arbitrary intersection shape in a robotic (humanoid) posture generation problem. The contact shape is the outcome of our posture generator that is formulated as a non-linear optimization programming to fulfill a large variety of robot intrinsic limitations (e.g. joint and torque limits) and tasks (e.g. desired contact). Starting by defining convex areas of contact on the robot's body and the environment, that we call contact patches, we can generate contacts with arbitrary intersection of a pair of any of these predefined patches. Our geometric contact modeling writes very simply as additional constraints and variables added to the optimization problem, translating the search for an ellipse inscribed in the intersection of the pair of patches we want in contact. The result of our posture generator is then a configuration where contact patches are not necessarily included in one another. This allows our posture generator to propose contacts of different shapes with a non-predefined number of contact points (used later to compute reaction/contact forces). We illustrate the efficiency of our method in multi-contact posture generation with the HRP-2 and ATLAS humanoid robots with results that can not be generated automatically by existing methods.

We propose a new bounding volume that achieves a tunable strict convexity of a given convex hull. This geometric operator is named sphere-tori-patches bounding volume (STP-BV), which is the acronym for the bounding volume made of patches of spheres and tori. The strict convexity of STP-BV guarantees a unique pair of witness points and at least C1continuity of the distance function resulting from a proximity query with another convex shape. Subsequently, the gradient of the distance function is continuous. This is useful for integrating distance as a constraint in robotic motion planners or controllers using smooth optimization techniques. For the sake of completeness, we compare performance in smooth and nonsmooth optimization with examples of growing complexity when involving distance queries between pairs of convex shapes.

In this paper, a first step is taken towards using vision in human-humanoid haptic joint actions. Haptic joint actions are characterized by physical interaction throughout the execution of a common goal. Because of this, most of the focus is on the use of force/torque-based control. However, force/torque information is not rich enough for some tasks. Here, a particular case is shown: height stabilization during table carrying. To achieve this, a visual servoing controller is used to generate a reference trajectory for the impedance controller. The control law design is fully described along with important considerations for the vision algorithm and a framework to make pose estimation robust during the table carrying task of the humanoid robot. We then demonstrate all this by an experiment where a human and the HRP-2 humanoid jointly transport a beam using combined force and vision data to adjust the interaction impedance while at the same time keeping the inclination of the beam horizontal.

In this paper, we propose a control scheme that allows a humanoid robot to perform a transportation task jointly with a human partner. From the study of how human dyads achieve such a task, we have developed a control law for physical interaction that unifies standalone and collaborative (leader and follower) modes for trajectory-based tasks. We present it in the case of a linear impedance controller but it can be generalized to more complex impedances. Desired trajectories are decomposed into sequences of elementary motion primitives. We implemented this model with a Finite State Machine associated with a reactive pattern generator. First experiments conducted on a real HRP-2 humanoid robot assess the overall approach.

We propose three novel methods to evaluate a distance function for robotic motion planning based on semiinfinite programming (SIP) framework; these methods include golden section search (GSS), conservative advancement (CA) and a hybrid of GSS and CA. The distance function can have a positive and a negative value, each of which corresponds to the Euclidean distance and penetration depth, respectively. In our approach, each robot's link is approximated and bounded by a capsule shape, and the distance between some selected link pairs is continuously evaluated along the joint's trajectory, provided by the SIP solver, and the global minimum distance is found. This distance is fed into the SIP solver, which subsequently suggests a new trajectory. This process is iterated until no negative distance is found anywhere in the links of the robot.We have implemented the three distance evaluation methods, and experimentally validated that the proposed methods effectively and accurately find the global minimum distances to generate a self-collision-free motion for the HRP-2 humanoid robot. Moreover, we demonstrate that the hybrid method outperforms other two methods in terms of computational speed and reliability.

In this paper, we propose a control scheme that allows a humanoid robot to perform a complex transportation scenario jointly with a human partner. At first, the robot guesses the human partner's intentions to proactively participate to the task. In a second phase, the human-robot dyad switches roles: the robot takes over the leadership of the task to complete the scenario. During this last phase, the robot is remotely controlled with a joystick. The scenario is realized on a real HRP-2 humanoid robot to assess the overall approach.

We present an experiment we carried out to determine the influence of the video feedback on the braincomputer interface performance of a system we designed to steer a humanoid robot. The interface is based on the wellknown steady-state visually evoked potentials and the stimuli are integrated into the live feedback from the robot's embedded camera. Five users controlled the HRP-2 humanoid in an experiment designed to measure the performance of the intentions' recognition system. A novel approach in the training phase is also experimented to understand and compensate performance loss due to the dynamic nature of the video feedback of the robot during walking motions. It results that this feedback induces a performance loss; we propose an effective solution to overcome this problem. The detailed results of these experiments are reported in this paper and we discuss the possible causes of performance loss under such conditions.

We propose a generalized framework together with an algorithm to plan a discrete sequence of multi-contact stances that brings a set of collaborating robots and manipulated objects from a specified initial configuration to a desired goal through non-gaited acyclic contacts with their environment or among each other. The broad range of applications of this generic algorithm includes legged locomotion planning, whole-body manipulation planning, dexterous manipulation planning, as well as any multi-contact-based motion planning problem that might combine several of these sub-problems. We demonstrate the versatility of our planner through example scenarios taken from the aforementioned classes of problems in virtual environments.

This article combines programming by demonstration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. Learning a task model allows the robot to anticipate the partner's intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compensate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm that tunes the impedance parameters, so as to ensure accurate reproduction. To facilitate the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained with simulation of a dyad of two planar 2-DOF robots.

Our objective in this work is to synthesize dynamically consistent motion for a simulated humanoid robot in acyclic multi-contact locomotion using multi-objective control. We take as an input a planned sequence of static postures that represent the contact configuration transitions; a multi-objective controller then synthesizes the motion between these postures, the objectives of the controller being decided by a finite-state machine. Results of this approach are presented in the attached video in the form of playback motions generated through non-real-time constraint-based dynamic simulations.

This paper examines the control continuity in hierarchical task-space controllers. While the continuity is ensured for any a priori fixed number of tasks -even in ill-conditioned configurations-, the control resulting from a hierarchical stack-of-task computation may not be continuous under some discrete events. In particular, we study how the continuity of the stack-of-task control computation is affected under discreet scheduling operations such as on-the-fly priority switching between tasks, or tasks insertion and removal, which changes the number of tasks in the stack controller. Different ways to formulate a hierarchy of tasks are presented together with their continuity properties, which is thoroughly analyzed under such discreet scheduling operations.

This paper proposes a new computation method to solve semi-infinite optimization problems for motion planning of robotic systems. Usually, this problem is solved by means of time-grid discretization of the continuous constraints. Unfortunately, discretization may lead to unsafe motions since there is no guarantee of constraint satisfaction between time samples. First, we show that constraints such as joint position and velocity do not need time-discretization to be checked. Then, we present the computation method based on Taylor polynomials to evaluate more complex constraints over time-intervals. This method also applies to continuous equality constraints, to continuous maximum derivative constraint, and to compute the cost function.

This paper deals with the modeling and instrumentation design for position and torque control of a new designed 4 degrees of freedom (DOF) haptic device to be used in a virtual reality laparoscopic simulator or as an input device for telepresence applications. Low-cost instrumentation is designed for torque/force control using no-contact current sensors and a force sensor. The relations between the applied force/torques and the output voltages are determined by calibration that shows that such low-cost sensors perform just as well as highly accurate sensors. The forward and inverse kinematics models of the 4-DOF are also formulated explicitly by using analytical methods. Based on this models and by introducing the kinematics constraints defined for the parallel part of the device, position control is performed successfully using PD controller. We carried out a PID controller for efficient torque control thanks to the introduced torque sensors. Experimental results that validate and encourage the proposed design are presented.

STP-BV is a bounding volume made of patches of spheres and toruses. These patches are assembled so that a convex polyhedral hull is bulged, in a tunable way, into a strictly convex form. Strict convexity ensures at least C1property of the distance function -and hence, its gradient continuity. STP-BV were introduced in our previous work [1], but proximity distance queries were limited to pairs of STP-BV covered objects. In this work we present an alternative to achieve fast proximity distance queries between a STP-BV object and any other convex shape. This is simply made by proposing a support mapping for STP-BV to be used with GJK algorithm [2] and its EPA extension to penetration cases [3]. Implementation and experiments of the proposed method and its performance are demonstrated with potential applications to robotics and computer graphics.

We present a motion planning algorithm that computes rough trajectories used by a contact-points planner as a guide to grow its search graph. We adapt collision-free motion planning algorithms to plan a path within the guide space, a submanifold of the configuration space included in the free space in which the configurations are subject to static stability constraint. We first discuss the definition of the guide space. Then we detail the different techniques and ideas involved: relevant C-space sampling for humanoid robot, task-driven projection process, static stability test based on polyhedral convex cones theory's double description method. We finally present results from our implementation of the algorithm.

A novel approach is presented which aims at building autonomously visual models of unknown objects, using a humanoid robot. Previous methods have been proposed for the specific problem of the next-best-view during the modeling and the recognition process. However our approach differs as it takes advantage of humanoid specificities in terms of embedded vision sensor and redundant motion capabilities. In a previous work, another approach to this specific problem was presented which relies on a derivable formulation of the visual evaluation in order to integrate it with our posture generation method. However to get rid of some limitations we propose a new method, formulated using two steps: (i) an optimization algorithm without derivatives is used to find a camera pose which maximizes the amount of unknown data visible, and (ii) a whole robot posture is generated by using a different optimization method where the computed camera pose is set as a constraint on the robot head.

We propose a solution which combines haptic sensing with safe interaction, at low cost. Contact locations are made through a flexible sheet of tactile binary switch matrix. This sheet covers the surface of a rigid bumper module assembled to the robot's basic link through a distributed pressure sensing units. Combination of location and force provides the haptic sensing module. The haptic system is covered with a flexible outer material which role is to absorb contact impacts and to cast local surface profile on which the robot can take support. This overall system allows having a combined haptic sensing with safe and robust physical interaction with both the environment and the human using active compliance. We discuss the benefit of such a simple and modular concept and present how to design the cover material. A simple prototype is realized and experienced.

This video illustrates our work on contact points planning and its recent enhancement by two new functionalities. First, by taking advantage of the possibilities offered by our initial posture generator, we include additional tasks in the planning that are not related to locomotion. Second, we refine the potential function that guides the planner so as to cope with more challenging scenarios. We then test these novelties on difficult problems with success, and experiment the output of one of the planned scenario on a HRP-2 humanoid robot.

This paper extends our previous work on contact points planning in two ways. First, by taking advantage of the possibilities offered by our initial posture generator, we include additional tasks that are not related to locomotion within the planning. The output motion will be generated so as to cope with these tasks. Second, we refine the potential function that guides the planner by introducing potential fields acting each on a single body. This helps escaping local minima of the original potential field and thus to deal with more challenging scenarios. We then test these novelties on difficult problems with success, and experiment the output of one of the planned scenario on a HRP-2 humanoid robot.

This paper is the continuation of our previous work in intercontinental, collaborative teleoperation with a humanoid robot. Our new achievement consists in an extension of the former single-arm bilateral teleoperation setting to include bimanual manipulation and walking. A coupling scheme for simultaneous manipulation and locomotion is developed. Furthermore, a task-based control framework, including a force-based control for the arms as well as a walking pattern generation, is presented to realize stable whole-body motions of the highly redundant humanoid robot. Experiments have been performed to assess the proposed control scheme. They bring to light additional scientific challenges that remain in order to reach a smooth and natural telepresent collaboration.

This paper presents a method for sequencing a set of robotic tasks in an optimal way. Tasks description and execution are based on the task-function approach, which enables to build complex whole-body behaviors from local control laws. A naive solution to this problem would be to schedule the execution of the tasks sequentially, avoiding concurrency. This solution does not exploit full robot capabilities such as redundancy and have poor performance in terms of execution time or energy. However, reasoning on concurrent tasks is difficult while accounting for all the physical constraints of the robot. Our contribution is to determine the time-optimal realization of the mission taking into account robotic constraints that may be as complex as collision avoidance. Our approach achieves more than a simple scheduling; its originality lies in maintaining the task approach in the formulated optimization of the task sequencing problem. This theory is exemplified through a complete experiment on the real HRP-2 robot.

This paper presents a model that describes physical interactions during dyadic collaborative tasks. This model is based on a homotopy between two controllers and defines the behavior of each partner as the result of a time-varying balance between two roles: the leader role, which consists in acting according to a plan without considering the other partner's intentions; and the follower role, which conversely consists in acting only based on the intentions of the other partner. The continuous switch between these two attitudes is described by two variables whose time-profile can define a task signature. After a brief presentation of the model, two illustrative scenarios are detailed to give more insights on how the homotopy parameter can be used to describe different situations that can occur in collaborative tasks between two partners. We especially focus on how some recent results in the human-human interaction can be encompassed by our proposed model. Experiments are performed to assess the usability of the model as a control scheme to implement advanced collaborative behaviors on a robotic platform.

This paper proposes a real-time implementation of collision and self-collision avoidance for robots. On the basis of a new proximity distance computation method which ensures having continuous gradient, a new controller in the velocity domain is proposed. The gradient continuity encompasses no jump in the generated command. Included in a stack of tasks architecture, this controller has been implemented on the humanoid platform HRP-2 and experienced in a grasping task while walking and avoiding collisions with the environment and auto-collisions.

This paper addresses the problem of generating dynamic motion for a humanoid between two predefined postures. The humanoid robot starts its motion from a statically stable configuration to another known statically stable configuration when it is necessary to make step; that is, the center of mass (COM) is inside the support polygon at the initial and the goal configurations. A dynamic motion generation method is proposed in order to generate the whole-body robot motion. Zero moment point preview control is used for maintaining dynamic stability while the robot is taking the step and suitable splines are used for generating the hands, neck and foot motion because it is a free motion on the field of gravity; the screw theory is applied under Lie groups for kinematics and dynamics humanoid modeling, which allows smooth and natural humanoid motion. Successful results on the HRP-2 humanoid robot are shown and discussed.

This paper presents a theoretical and experimental study of a model describing the heat exchange between a fingertip and any given touched object. This model is solved and implemented on a thermal display incorporated into a haptic device. Thermal feedback can assist in object identification, representation or in the creation of a complete haptic signature of a given object. Alternatively, thermal feedback could be used as a sensory substitute or adjunct for visual or tactile feedback. Experimental results validating the proposed thermal model are evaluated when touching virtual objects in virtual environments.

In multimodal tele-cooperation as considered in this paper two humans in distant locations jointly perform a task requiring multimodal including haptic feedback. One human operator teleoperates a remotely placed humanoid robot which is collocated with the human cooperator. Time delay in the communication channel as destabilizing factor is one of the multiple challenges associated with such a tele-cooperation setup. In this paper we employ a control architecture with force-position exchange accounting for the admittance type of the haptic input device and the telerobot, which both are position-based admittance controlled. Llewellynpsilas stability criteria are employed for the parameter tuning of the virtual impedances in the presence of time delay. The control strategy is successfully validated in an intercontinental tele-cooperation experiment with the humanoid telerobot HRP-2 located in Japan/Tsukuba and a multimodal human-system-interface located in Germany/Munich, see also the corresponding video submission. The proposed setup gives rise to a large number of exciting new research questions to be addressed in the future.

The video shows an intercontinental cooperative telemanipulation task, whereby the operator site is located in Munich, Germany and the teleoperator site in Tsukuba, Japan. The human operator controls a remotely located teleoperator, which performs a task in the remote environment. Hereby the human operator is assisted by another person located at the remote site. The task consists in jointly grasping an object, moving it to a new position and finally releasing it, see Fig. 1.

In this paper we present an integrative frame work centered on haptic interaction with virtual avatars. This framework is devised for general prototyping and collaborative scenario studies with haptic feedback. First we present the software architecture of the framework and give details on some of its components. Then we show how this framework can be used to derive in a short time a virtual reality simulation. In this simulation, a user directly interacts with a virtual avatar to collaboratively manipulate a virtual object, with haptic feedback and using fast dynamics computation and constraint based methods with friction.

This paper describes a motion generation method for dynamic lifting by a humanoid robot. The proposed technique suggests the possibility of taking advantage of the whole body motion in order to facilitate the lifting movement. In particular, the idea is to perform a preliminary motion in order to generate a momentum which is instantaneously transferred to the object as an impulsive force. This allows the humanoid to lift up an object that could not be lifted up only by continuous force. However an impulsive force may make the humanoid unstable. Then, we propose to set the center of percussion (CoPn) of the whole system at the center of the support polygon of the humanoid when it lifts up the object. We also propose a design method of a preliminary motion of the humanoid that generates a sufficient momentum to lift up an object without any slip, tumble and hop of the whole system. The effectiveness of the proposed method is confirmed by simulation and experiment.

This paper presents a haptic device that has been conceived to render both kinesthetic and thermal sensations computed from operator interaction with a 3D virtual environment. Computer simulation is based on a precise modeling algorithm of the thermal exchange between a bare finger and an object. The rendering is based on a closed loop control of both temperature and thermal flow signals. Each module composing the overall device and the rendering process are thoroughly described. We show that the whole system exhibits good technical performances in 3D multimodal interactive simulation

This paper deals with the motion planning of a polyarticulated robotic system for which support contacts are allowed to occur between any part of the body and any part of the environment. Starting with a description of the environment and of a target, it computes a sequence of postures that allow our system to reach its target. We describe a very generic architecture of this planner, highly modular, as well as a first implementation of it. We then present our results, both simulations and real experiments, for a simple grasping task using the HRP-2 humanoid robot

This paper addresses the role of toe joints in increasing the walking speed of biped robots. It is worthy that adding a toe joint will increase the step length thanks to the additional degree of freedom. But, the originality of this work is that longer steps are obtained thanks to an under-actuated phase and an appropriate ZMP trajectory. The simulations showed that adding passive toe joints allows smoother and 1.5 faster walking

A new computer haptics algorithm to be used in general interactive manipulations of deformable virtual objects is presented. In multimodal interactive simulations, haptic feedback computation often comes from contact forces. Subsequently, the fidelity of haptic rendering depends significantly on contact space modeling. Contact and friction laws between deformable models are often simplified in up to date methods. They do not allow a "realistic" rendering of the subtleties of contact space physical phenomena (such as slip and stick effects due to friction or mechanical coupling between contacts). In this paper, we use Signorini's contact law and Coulomb's friction law as a computer haptics basis. Real-time performance is made possible thanks to a linearization of the behavior in the contact space, formulated as the so-called Delassus operator, and iteratively solved by a Gauss-Seidel type algorithm. Dynamic deformation uses corotational global formulation to obtain the Delassus operator in which the mass and stiffness ratio are dissociated from the simulation time step. This last point is crucial to keep stable haptic feedback. This global approach has been packaged, implemented, and tested. Stable and realistic 6D haptic feedback is demonstrated through a clipping task experiment.

This paper presents a new method and formalism to achieve thermal feedback for teleoperation and telepresence applications. The basic idea is to realize an approach similar to force reflecting telerobotics. That is to say, bilateral coupling between thermal temperature and thermal flux variables. Thermal properties and exchange during contact are modeled with their relating equations. Similarly to force feedback coupling, four potential controllers are possible; we implemented all of them using stable and linear control schemes. Experimental results show that some coupling are superior in terms of thermal transparency and stability. We used two Peltier heat pumps as thermal sources; the one serving as a thermal display while the other one is to play the role of a "robotic finger".

Tactile displays are deformable surfaces that simulate skin deformations that occur when interacting with real surfaces (relieves) by transmitting small-scale shape information and vibration to the fingertip. We present a new vibrotactile display system composed of a matrix of 8/spl times/8 tactile display, a control/power system (electrical hardware) and a software interface dedicated to translate and display a black and white picture on the 8/spl times/8 tactile display with different close-ups. The 8/spl times/8 tactile display is based on a new multi-layer concept. This concept allows developing a new generation of tactile matrices with a high density of micro actuators, a simple assembling process and at a quite competitive price.

Tactile displays simulate skin deformations that occur when interacting with real surfaces (relieves) by transmitting small-scale shape information and vibration to the fingertip. In this paper, we present a new tactile interface VT (vector-touch) that is based on a rod network positioned in parallel to each other, electric motors to induce a rotation and a positioning mechanism that allows rotating rods to be positioned at different interspaces. Using a rather limited number of actuators, the rods can be driven in such manner to obtain various types of relieves or surface qualities.

This paper presents the characterization in terms of force and displacement of a new interpenetrated network conductive polymer (IPN-CP) to be used as an actuator that works in air conditions. This last property opens many potential applications in robotics, bionics, haptics interface technology (namely tactile feedback devices), etc. Different experimental investigations show that the proposed actuator exhibits acceptable force behavior and capabilities (relatively to its size and mass) when classical solvents are used. However, IPN-CP with ionic liquid solvents show poor capabilities. Since ionic solvents guarantee long life usability, we found it somehow disappointing namely for robotic applications. We give some explanations for that, but we believe that this drawback could mid solutions in the near future.

The paper addresses the design of an inclusive haptic display based on a multilevel display concept. The end version prototype will operate with bare hand/finger interaction to serve virtual reality applications, psychological and psychophysics studies on haptic perception, and computer haptics including combined texture/kinesthetic rendering algorithms. The proposed concept is an alternative design that brings together different actuation technologies in order to replicate, with high-fidelity, haptic sensation. Other potentials, such as the possibility to blend the haptic and the visualization spaces, are also demonstrated by this concept.

In this paper we consider deformable objects in haptic simulations. The physical simulation that drives haptic perception requires a good dynamic behavior. The inputs of the deformable model come from the treatment of the collision. We propose to focus on the contact restitution between deformable objects to guarantee "physical and perceptual realisms" of the haptic feedback. Signorini, in 1933, proposed a physical model of contact for deformable objects interacting with rigid static bodies (Signorini, S, 1933). This paper shows that the Signorini's model extents to contacts between two deformable objects using Gauss-Seidel resolution of complementarity problems. An interactive resolution of the overall formulation is presented and experienced on deformable objects using the finite linear-elements method.

This paper discusses preliminary design of an interactive childbirth simulator with haptic feedback. This exploratory work started following a demand of the obstetrics and gynecology service of a Parisian hospital. Ideally, the final system should integrate cases-study database in order to provide a powerful teaching media by means of best of the virtual/augmented realities technology in terms of multimodal visualization and display. The difficulty of this new system lies in the haptic display function allowing to teach gesture interaction skill to obstetricians/midwifes students. This paper deals only on the feasibility of such a system. First, the system is presented and its "nominal ingredients" described in generic terms. Simple models of women pelvis, fetus and muscles have been considered. Pilot force feedback delivery is simulated and experienced; results are discussed.

Most of well-known approaches for rigid body simulations are formulated in the contact-space. Due to Gauss' principle of least constraints, the frictionless dynamics problems are formulated in a motion-space. While the two formulations are mathematically equivalent, they are not computationally equivalent. The motion-space formulation is better conditioned, always sparse, needs less memory, and avoids some unnecessary computations. A preliminary experimental comparison suggests that an algorithm operating in the motion-space takes advantage of sparsity to perform increasingly better than a contact-space algorithm as the average number of contact points per object increases.

A few years ago, Vanecek (1994) suggested to apply a variant of back-face culling to speed-up collision detection between polyhedral objects. However, Vanecek's method is linear in the number of faces in the object, which is unpractical for large models. This paper suggests to add some geometrical information to hierarchies of bounding volumes, typically used in collision detection, and perform conservative back-face culling at the bounding-volume level in constant time. The method described in this paper can be applied to complement any kind of bounding-volumes hierarchy and allows a trade-off between memory and speed. Preliminary experimental results suggest that the method allows a significant speed-up, especially in close proximity situations.

This paper introduces a fast continuous collision detection technique for polyhedral rigid bodies. As opposed to most collision detection techniques, the computation of the first contact time between two objects is inherently part of the algorithm. The method can thus robustly prevent objects interpenetrations or collisions misses, even when objects are thin or have large velocities. The method is valid for general objects (polygon soups), handles multiple moving objects and acyclic articulated bodies, and is efficient in low and high coherency situations. Moreover, the method can be used to speed up existent continuous collision detection methods for parametric or implicit rigid surfaces. The collision detection algorithms have been successfully coupled to a real-time dynamics simulator. Various experiments are conducted that show the method's ability to produce high-quality interaction (precise objects positioning for example) between models up to tens of thousands of triangles, which couldn't have been performed with previous continuous methods. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation - Virtual Reality

Overlaying classical teleoperation control schemes based on a bilateral master-slave coupling, a teleoperation architecture designed in a general teleworking context is proposed. In this scheme, the executing machine is perceptually and functionally hidden to the operator by means of an intermediate functional representation between a real remote world and man. As any executing machine, and more particularly a robot, will be replaced by man, the image of the robot will not appear in the intermediate representation. This principle is thus named: "the hidden robot concept." In this approach, the teleoperation problem is divided into two main parts: 1) choosing the appropriate intermediate representation and determining its interaction and relation with man and 2) building the relations and transformations between the intermediate representation and the real remote environment. The constituents of this teleoperator are outlined in this paper and an experiment validating this concept is presented.

This paper describes a new collision detection algorithm designed for interactive manipulation in virtual environments. Making some assumptions on object motion, the collision time between two objects can be computed by solving a polynomial equation whose degree is equal to or smaller than three.

This paper considers whether a passive isometric input device, such as a Spaceball/sup TM/, used together with visual feedback, could provide the operator with a pseudo-haptic feedback. For this aim, two psychophysical experiments have been conducted. The first experiment consisted of a compliance discrimination, between two virtual springs hand-operated by means of the Spaceball/sup TM/. In this experiment, the stiffness (or compliance) JND turned out to be 6%. The second experiment assessed stiffness discrimination between a virtual spring and the equivalent spring in reality. In this case, the stiffness (or compliance) JND was found to be 13.4%. These results are consistent with previous outcomes on manual discrimination of compliance. Consequently, this consistency reveals that the passive apparatus that was used can, to some extent, simulate haptic information. In addition, a final test indicated that the proprioceptive sense of the subjects was blurred by visual feedback. This gave them the illusion of using a nonisometric device.

While active work is being achieved in order to conceive very transparent, stable and user-friendly human-machine haptic interfaces, few works address the human factors and performance evaluation of haptic devices while being used in an actual operator interaction with a virtual environment. The article addresses the above aspects.

Teleoperators using an intermediate functional representation of the remote real environment (RE), suffer the lack of accurate synthetic modeling. Discrepancies will always occur between the RE and its artificial representation by means of virtual environment (VE). A strategy to deal with VE/RE uncertainties based mainly on sensory level interpretation is presented. It is directed to avoid the use of task knowledge by providing a stream of virtual sensors values. Within the remote site, a supervisor is in charge of recovering from the VE/RE discrepancies by a continuous simulated and real states comparison. The states are derived from simulated and real sensor interpretations respectively. A simple experiment with the proposed approach is presented using only position (velocity) and force sensors. Limitations of the proposed approach are also discussed.

This paper discusses the development of new teleoperator systems. While many innovations during the last decade made teleoperation technology progress, some severe well known lacks that we enumerate still persist. With respect to some attractive solutions proposed for coping with these problems we designed a bilateral control scheme based on what we called the hidden robot concept. The teleoperator achieves tasks manually in a natural way within a virtual environment (VE). Thanks to suitable bilateral transformations, the virtual tasks are being reproduced by any slave robot within the remote site. Mainly task based, our approach is not considered like a high level task knowledge based control. Rather, we consider it like a more refined shared autonomy control with a high level abstraction interface. Three main components are developed: (i) supervision loop, (ii) bilateral transformation layer, (iii) execution loop. The approach has been validated experimentally and preliminary results as well as further work are discussed.