Conforming to safety standards often limits collaborative robots' performance and size, restricting their applications despite their capabilities. Planning their motions in human environments involves a trade-off between optimal trajectory planning and quick adaptation to dynamic, unstructured spaces. Traditional trajectory planning methods either use simplified robot models and sacrifice robot's abilities for computational efficiency, or exploit robots' abilities fully but have high computational complexity and rely on substantial pre-computation. This paper introduces an approach for trajectory planning that exploits robot's full motion abilities while planning on-the-fly. In each step of the trajectory execution, it evaluates robot's movement ability using polytope algebra and calculates a time-optimal Trapezoidal Acceleration Profile (TAP) on the remaining trajectory. The method is shown to be near time-optimal (around 5% slower trajectories) by benchmarking it against the state-of-the-art time-optimal method TOPP-RA. The method allows reaching higher velocities (able to plan up to 100% of the robot's kinematic limits) while at the same time lowering the tracking error (under 4mm) than traditional Cartesian Space planning methods. A mock-up experiment demonstrates its efficiency in collaborative waste sorting using a Franka Emika Panda robot.

Accurate physical simulation is crucial for the development and validation of control algorithms in robotic systems. Recent works in Reinforcement Learning (RL) take notably advantage of extensive simulations to produce efficient robot control. State-of-the-art servo actuator models generally fail at capturing the complex friction dynamics of these systems. This limits the transferability of simulated behaviors to real-world applications. In this work, we present extended friction models that allow to more accurately simulate servo actuator dynamics. We propose a comprehensive analysis of various friction models, present a method for identifying model parameters using recorded trajectories from a pendulum test bench, and demonstrate how these models can be integrated into physics engines. The proposed friction models are validated on four distinct servo actuators and tested on 2R manipulators, showing significant improvements in accuracy over the standard Coulomb-Viscous model. Our results highlight the importance of considering advanced friction effects in the simulation of servo actuators to enhance the realism and reliability of robotic simulations.

The presented work tackles the question of quantifying the pose deviations of robots subject to external disturbance forces. While this question may not be central for large robots perfectly rejecting disturbances through high controller gains, it is an important factor when considering collaborative settings where smaller robots may be deviated from their task because of unmodeled physical interactions. This is all the more true with human-robot collaboration where human capacities may fluctuate over time and have to be compensated by a proper adaptation of the robot control. To move forward in this direction, this work first derives a deviation prediction methodology and exemplifies it using three largely employed control approaches. The proposed prediction method is then validated using simulated and real robot experiments both in single and multiple robots cases. The obtained results constitute a stepping stone towards a quantitative metric for robots adapting their behaviour to human motor fluctuation.

In this paper, the concept of signaling motions of a robot interacting with a human is presented. These motions consist in using the redundant degrees of freedom of a robot performing a task as new means of meaningful robot-human communication. They are generated through quasi-static torque control, in consistency with the main robot task. A double within-subject (N=16) study is conducted to evaluate the effects of two signaling motions on the performance of a task by participants and on their behavior towards the robot. Our results show a positive effect on both the task execution and the participants behavior. Additionally, both signaling motions seem to improve the situation awareness of the participants by fueling their mental model throughout the interaction.

Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionally impacts a rigid surface, we investigate the possibility and accuracy of predicting the post-impact robot velocity from the pre-impact velocity and impact configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact.The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the readily available rigid-body robot model, that presents no oscillations and that is the one typically obtained via mainstream robot simulator software. The results of this new approach are promising in terms of prediction accuracy and thus relevant for the growing field of impact-aware robot control. The recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate interest/research in this field.

Ellipsoid-based manipulability measures are often used to characterize the force/velocity task-space capabilities of robots. While computationally simple, this approach largely approximate and underestimate the true capabilities. Force/velocity polytopes appear to be a more appropriate representation to characterize the robot’s task-space capabilities. However, due to the computational complexity of the associated vertex search problem, the polytope approach is mostly restricted to offline use, e.g. as a tool aiding robot mechanical design, robot placement in work-space and offline trajectory planning. In this paper, a novel on-line polytope vertex search algorithm is proposed. It exploits the parallelotope geometry of actuator constraints. The proposed algorithm significantly reduces the complexity and computation time of the vertex search problem in comparison to commonly used algorithms. In order to highlight the on-line capability of the proposed algorithm and its potential for robot control, a challenging experiment with two collaborating Franka Emika Panda robots, carrying a load of 12 kilograms, is proposed. In this experiment, the load distribution is adapted on-line, as a function of the configuration dependant task-space force capability of each robot, in order to avoid, as much as possible, the saturation of their capacity.

In this work, a robotic assistance strategy is developed to improve the safety in an artisanal task that involves a strong interaction between a machine-tool and an operator. Wood milling is chosen as a pilot task due to its importance in carpentry and its accidentogenic aspect. A physical model of the tooling process including a human is proposed and a simulator is thereafter developed to better understand situations that are dangerous for the craftsman. This simulator is validated with experiments on three subjects using an harmless mock-up. This validation shows the pertinence of the proposed control approach for the collaborative robot used to increase the safety of the task.

Despite the many advances in collaborative robotics, collaborative robot control laws remain similar to the ones used in more standard industrial robots, significantly reducing the capabilities of the robot when in proximity to a human. Improving the efficiency of collaborative robots requires revising the control approaches and modulating online and in real-time the low-level control of the robot to strictly ensure the safety of the human while guaranteeing efficient task realization. In this work, an openly simple and fast optimization based joint velocity controller is proposed which modulates the joint velocity constraints based on the robot's braking capabilities and the separation distance. The proposed controller is validated on the 7 degrees-of-freedom Franka Emika Panda collaborative robot.

In this paper, a control solution featuring an energetic constraint is developed to improve the safety of a robotic manipulator sharing its workspace with humans. This general control structure, exploits a generic safe controller that ensures the respect of multiple constraints thanks to a Linear Quadratic Problem formulation. With a unified energetic formulation, the controller allows to explicitly limit both the kinetic energy when moving and the wrench applied to the environment in case of contact with an unexpected obstacle. This control approach is experimented on a redundant Kuka LWR4+ robot which end-effector shall precisely point toward a given location while following a trajectory.

Redundant robots performing multiple tasks of different priority levels are often handled by hierarchical control frameworks. This paper proposes a general hierarchical control approach that can handle not only a single standard lexicographic hierarchy, but also a complex priority network involving both strict and non-strict task priorities. In this approach, priorities can be defined by pairs of tasks and are encoded by a priority matrix. Priority modulations are achieved by the regulation of a novel generalized projector, which takes the priority matrix as input. This projector allows a task to be completely projected in the null-space of a set of tasks, while partially projected in those of some other tasks. Such a projector can be used to achieve multiple priority rearrangements simultaneously. The effectiveness of this approach is demonstrated on a KUKA LWR robot performing task priority rearrangements as well as task insertion and deletion.

Humanoid robots are expected to act in human environments, where some of the contacts can be non-rigid. A fairly large amount of work has been devoted to the whole-body control of humanoids under rigid contacts, but few of them take into account non-rigid contacts. Indeed, the handling of unknown compliant contacts to achieve goal directed actions and whole-body balance remains a challenge. This paper addresses this problem by proposing a control mechanism that solves whole-body tasks under non-rigid contacts. It is a reactive control approach that automatically regulates contact forces and whole-body motions based on the motion of contact points without the awareness of the rigidity properties of the contact material. Verification of this approach is conducted through experiments on the iCub humanoid robot in simulation.

Whole-Body Control methods offer the potential to execute several tasks on highly redundant robots, such as humanoids. Unfortunately, task combinations often result in incompatibilities which generate undesirable behaviors. Prioritization techniques can prevent tasks from perturbing one another but often to the detriment of the lower precedence tasks. For many tasks, static prioritization is not necessary or even appropriate because tasks can often be achieved in variable ways, as in reaching. In this paper, we show that such task variability can be used to modulate task priorities during execution, to temporarily deviate certain tasks as needed, in the presence of incompatibilities. We first present a method for mapping from task variance to task priority and then provide an approach for computing task variance. Through three common conflict scenarios, we demonstrate that mapping from task variance to priorities reactively solves a number of task incompatibilities.

In this paper, we propose a physically meaningful energy-related safety indicator for robots sharing their workspace with humans. Based on this indicator, a safety criterion accounting for the breaking capabilities of the robot is included as a quadratic constraint in the control algorithm. This constraint is modulated by the distance between the human operator and the end-effector of the robot. The control algorithm is formulated as an optimization problem and computes the actuation torque of a robotic manipulator given some task to be performed and physical constraints to respect. The overall framework is validated in a physics simulation software on a Kuka LWR4 and different behaviours of the robot towards a considered obstacle in its environment are evaluated and discussed.

A novel formulation of the synthesis of motor coordination for humanoid whole-body motion is proposed in this paper, in order to ensure robust control of postural stability. It relies on the distributed model predictive control framework to coordinate, in an optimal way, several objectives. The effectiveness of this control technique to maintain postural stability of a biped against strong external disturbances is shown. Control of the horizontal dynamics of the center of mass can withstand limited perturbations. Thus postural stability criteria are specified with respect to the robot center of mass vertical and horizontal dynamics, and to the angular dynamics of its torso. Formulating the balance problem in a predictive form and distributing at different time scales significantly increases the robustness of the system to external disturbances, in terms of both tip-over and slippage risks. This original control architecture is validated through the simulation of an iCub robot performing a walking activity under unknown external actions.

Balance strategies range from continuous postural adjustments to discrete changes in contacts: their simultaneous execution is required to maintain postural stability while considering the engaged walking activity. In order to compute optimal time, duration and position of footsteps along with the center of mass trajectory of a humanoid, a novel mixed-integer model of the system is presented. The introduction of this model in a predictive control problem brings the definition of a Mixed-Integer Quadratic Program, subject to linear constraints. Simulation results demonstrate the simultaneous adaptation of the gait pattern and posture of the humanoid, in a walking activity under large disturbances, to efficiently compromise between task performance and balance. In addition, a push recovery scenario displays how, using a single balance-performance ratio, distinct behaviors of the humanoid can be specified.

In the last years, several regression algorithms have been proposed to learn accurate mechanical models of robots. Comparisons are proposed at the conceptual level or through the use of recorded databases, but they deliver limited conclusions with respect to the real performance of these algorithms in their true context of use, i.e. online learning on the real robot interacting with its environment, within a feedback control loop. In this paper, we provide an empirical study of three state-of-the-art regression methods through online learning on the iCub robot holding a tool. We show that they can effectively learn a visuo-motor kinematic model for a simple visual servoing task in a very limited time (few minutes), without making any a priori hypothesis on the geometry of the robot and its tool. Furthermore, we can draw from the results some stronger conclusions about the comparison of the algorithms than previous studies based on databases.

This paper proposes a robust whole-body control formulation for biped balance in disturbed conditions by manipulation tasks. In order to include the effects of the interaction of the robot with its environment, required by the manipulation task in the balance control, we introduce a distributed preview control which captures both balance and manipulation behaviors and enables the regulation of the interaction impedance. The initial ZMP preview control is extended to take into account the disturbance resulting from the manipulation task and the preview control of adaptive impedances used to drive the upper-limbs. The resulting behavior is illustrated in a simple scenario. Its aptitude to dynamically extract an optimal control strategy improving tracking performances of both manipulation and balance tasks is also assessed when complex perturbations have to be compensated.

We present a novel approach to deal with transitions while performing a sequence of dynamic tasks with a humanoid robot. The simultaneous achievement of several tasks cannot be ensured, so we use a strategy based on weights to represent their relative importance. The robot interacts with a changing environment, and the input torques are different depending on whether the robot performs tasks in a constrained state (e.g. in contact) or not. We develop a solution with smooth weights variations and transitional tasks which avoids sharp torque evolutions. In order to validate this approach, simulations are carried out on a virtual iCub robot which is assigned the realization of a complex mission involving various changing tasks.

Most of the literature reactive control laws have much difficulties to handle properly constraints such as joint limits, obstacles and saturations, including them as equalities in the Inverse Velocity Kinematics (IVK) problem. Actually, it seems relevant to handle them through inequalities, as the constraints are more numerous than the number of DOFs. However, the intuitive constraints expression can lead to incompatibilities between constraints, which necessarily induces constraints violations. This paper brings two distinct contributions. First, the usual constraints inequalities are modified to make them compatible permanently. Second, an intuitive and efficient constraint compliant control law is proposed.

We present an adaptive control approach combining forward kinematics model learning methods with the operational space control approach. This combination endows the robot with the ability to realize hierarchically organised learned tasks in parallel, using tasks null space projectors built upon the learned models. We illustrate the proposed method on a simulated 3 degrees of freedom planar robot. This system is used as a benchmark to compare our method to an alternative approach based on learning an extended Jacobian. We show the better versatility of the retained approach with respect to the latter.

This work is devoted to planning and execution of complex missions in robotics. Robotics has evolved from an industrial, repetitive framework to application domains with much more variability of tasks, with increasing complexity in uncertain environment. This is clearly the case for service robotics for example, but even industrial robots have now to work in environment not totally calibrated for the task they have to perform. The result is that the classical decomposition in static ordered local tasks cannot apply in presence of such a variability. It has a poor dynamic performance and cannot cope with uncertainties. Our work is organized around a complex mission: "Go to the blackboard and write" for mobile manipulators that have capabilities of locomotion and manipulation. It is a simple and intuitive example of a complex mission that relies on different sensors, exhibits different operating modes and needs to switch between different feedbacks and set-points. Our approach is based on hybrid dynamical systems. It is focused on dynamical sequencing of control laws that ensures good transients, robustness and allows to update the mission at every transition from one mode to another. Simulations have been realized with Matlab Simulink and Stateflow toolboxes and an experimental validation has been developed within the controller on the H/sub 2/bis nonholonomic mobile manipulator.