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.

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.

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.

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.

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.

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 address practical issues to develop reactive motion planning method capable of replanning the path online when the environment changes during the execution. By introducing planning and execution threads running in parallel, the robot can keep moving even during the replanning process as long as the motion is safe. The proposed method can be applied to discontinuous input of environmental changes that may occur due to incomplete perception. We then address a roadmap reuse method for efficient replanning to make use of the increasing knowledge about the environment, by introducing working and learning roadmaps. A general interface with robot motion controller is also defined so that the method can be applied to various types of robots. The proposed method is validated through planning simulations with moving obstacles.