226 research outputs found
Generation of dynamic motion for anthropomorphic systems under prioritized equality and inequality constraints
In this paper, we propose a solution to compute full-dynamic motions for a humanoid robot, accounting for various kinds of constraints such as dynamic balance or joint limits. As a first step, we propose a unification of task-based control schemes, in inverse kinematics or inverse dynamics. Based on this unification, we generalize the cascade of quadratic programs that were developed for inverse kinematics only. Then, we apply the solution to generate, in simulation, wholebody motions for a humanoid robot in unilateral contact with the ground, while ensuring the dynamic balance on a non horizontal surface
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Tasks prioritization for whole-body realtime imitation of human motion by humanoid robots
International audienceThis paper deals with on-line motion imitation of a human being by a humanoid robot using inverse kinematics (IK). First, the human observed trajectories are scaled in order to match the robot geometric and kinematic description. Second, a task prioritization process is defined using both equality and minimized constraints in the robot IK model, with four tasks: balance management, end-effectors tracking, joint limits avoidance and staying close to the human joint trajectories. The method was validated using the humanoid robot NAO
Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
We tackle the problem of developing humanoid loco-manipulation skills with
deep imitation learning. The difficulty of collecting task demonstrations and
training policies for humanoids with a high degree of freedom presents
substantial challenges. We introduce TRILL, a data-efficient framework for
training humanoid loco-manipulation policies from human demonstrations. In this
framework, we collect human demonstration data through an intuitive Virtual
Reality (VR) interface. We employ the whole-body control formulation to
transform task-space commands by human operators into the robot's joint-torque
actuation while stabilizing its dynamics. By employing high-level action
abstractions tailored for humanoid loco-manipulation, our method can
efficiently learn complex sensorimotor skills. We demonstrate the effectiveness
of TRILL in simulation and on a real-world robot for performing various
loco-manipulation tasks. Videos and additional materials can be found on the
project page: https://ut-austin-rpl.github.io/TRILL.Comment: Submitted to Humanoids 202
Prioritized motion-force control of constrained fully-actuated robots: "Task Space Inverse Dynamics"
Pre-print submitted to "Robotics and Autonomous Systems"We present a new framework for prioritized multi-task motion-force control of fully-actuated robots. This work is established on a careful review and comparison of the state of the art. Some control frameworks are not optimal, that is they do not find the optimal solution for the secondary tasks. Other frameworks are optimal, but they tackle the control problem at kinematic level, hence they neglect the robot dynamics and they do not allow for force control. Still other frameworks are optimal and consider force control, but they are computationally less efficient than ours. Our final claim is that, for fully-actuated robots, computing the operational-space inverse dynamics is equivalent to computing the inverse kinematics (at acceleration level) and then the joint-space inverse dynamics. Thanks to this fact, our control framework can efficiently compute the optimal solution by decoupling kinematics and dynamics of the robot. We take into account: motion and force control, soft and rigid contacts, free and constrained robots. Tests in simulation validate our control framework, comparing it with other state-of-the-art equivalent frameworks and showing remarkable improvements in optimality and efficiency
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
- …