3 research outputs found
Adaptive Whole-Body Manipulation in Human-to-Humanoid Multi-Contact Motion Retargeting
Submitted to Humanoids 2017International audienceWe propose a multi-robot quadratic program (QP) controller for retargeting of a human's multi-contact loco-manipulation motions to a humanoid robot. Using this framework , the robot can track complex motions and automatically adapt to objects in the environment that have different physical properties from those that were used to provide the human's reference motion. The whole-body multi-contact manipulation problem is formulated as a multi-robot QP, which optimizes over the combined dynamics of the robot and any manipulated objects. The multi-robot QP maintains a dynamic partition of the robot's tracking links into fixed support contact, manipulation contact, and contact-free tracking links, which are re-partitioned and re-instantiated as constraints in the multi-robot QP every time a contact event occurs in the human motion. We present various experiments (bag retrieval, door opening, box lifting) using human motion data from an Xsens inertial motion capture system. We show in full-body dynamics simulation that the robot is able to perform difficult single-stance motions as well as multi-contact-stance motions (including hand supports), while adapting to objects of varying inertial properties
Methods to improve the coping capacities of whole-body controllers for humanoid robots
Current applications for humanoid robotics require autonomy in an environment specifically
adapted to humans, and safe coexistence with people. Whole-body control is
promising in this sense, having shown to successfully achieve locomotion and manipulation
tasks. However, robustness remains an issue: whole-body controllers can still
hardly cope with unexpected disturbances, with changes in working conditions, or
with performing a variety of tasks, without human intervention. In this thesis, we
explore how whole-body control approaches can be designed to address these issues.
Based on whole-body control, contributions have been developed along three main
axes: joint limit avoidance, automatic parameter tuning, and generalizing whole-body
motions achieved by a controller. We first establish a whole-body torque-controller
for the iCub, based on the stack-of-tasks approach and proposed feedback control
laws in SE(3). From there, we develop a novel, theoretically guaranteed joint limit
avoidance technique for torque-control, through a parametrization of the feasible joint
space. This technique allows the robot to remain compliant, while resisting external
perturbations that push joints closer to their limits, as demonstrated with experiments
in simulation and with the real robot. Then, we focus on the issue of automatically
tuning parameters of the controller, in order to improve its behavior across different
situations. We show that our approach for learning task priorities, combining domain
randomization and carefully selected fitness functions, allows the successful transfer of
results between platforms subjected to different working conditions. Following these
results, we then propose a controller which allows for generic, complex whole-body
motions through real-time teleoperation. This approach is notably verified on the robot
to follow generic movements of the teleoperator while in double support, as well as to
follow the teleoperator\u2019s upper-body movements while walking with footsteps adapted
from the teleoperator\u2019s footsteps. The approaches proposed in this thesis therefore
improve the capability of whole-body controllers to cope with external disturbances,
different working conditions and generic whole-body motions