Hierarchical motor learning and synthesis with passivity-based controller and phase oscillator
ABSTRACT In this paper, we propose a simple framework for learning and synthesis of fast and complex motor tasks. Where a passivity-based task-space controller acts not only as a full-body force control module, but also as an important module to generate phasic joint patterns. The generated joint patterns are encoded into the parameters of phase oscillators and form the synergy of the task. Then, similar and/or faster motions are synthesized by superposing the task space controller output and the oscillator output with the modified oscillator amplitudes and/or frequencies. We present some examples of whole-body motion synthesis on a human-sized biped humanoid robot including squatting, dancing and stepping while bipedal balancing. The simulation and experimental videos are supplemented.
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Conference Paper: Integration of multi-level postural balancing on humanoid robots[Show abstract] [Hide abstract]
ABSTRACT: This paper discusses an integration issue of multi-level postural balancing on humanoid robot. We give a unified viewpoint of postural balancing, which covers Ankle Strategy to Hip Strategy. Two kinds of distributor of desired ground reaction force to whole-body joint torque are presented. The one distributor leads to a dynamic balancer which covers Hip strategy, with the under-actuated situation. A simple angular momentum regulator is also proposed to stabilize the internal motions due to the joint redundancy. The other distributor leads to a static balancer which lies between Ankle and Hip strategy. Furthermore, this paper demonstrates that replacement of the center of mass feedback with the local joint stiffness makes the robot much stabler for some fast motions. Motivated by the practicability of the static balancer and the strong push-recovery performance of the dynamic balancer, this paper presents a simple integration by superposition of the both balancers on a compliant human-sized biped robot. The simulation and experimental videos are supplemented.Robotics and Automation, 2009. ICRA '09. IEEE International Conference on; 06/2009
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ABSTRACT: We propose a new supervised learning and syn- thesis framework for fast and complex motor tasks. Wherein, a statics-based task-space controller acts not only as a full- body motion control module, but also as a module to generate synergetic joint patterns. The generated joint patterns are encoded into the parameters of phase trajectories of attractors and form the synergy of the task. Similar, but faster motions are synthesized by superposing the task-space controller output and the trajectory attractor output with the modified parameters, while learning dynamics and stiffness according to the task error. We demonstrate the proposed framework by simulating a balanced fast squat on a humanoid robot model.
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ABSTRACT: This paper presents a full-body compliant motor control strategy with a virtual musculoskeletal system for anthropomorphic robots. This integrates a task-space control module and a joint stiffness control module on joint torque control implementation. The passivity-based task-space controller manages the Cartesian forces and provides the robot with full-body compliance and balancing ability, and the joint stiffness controller locally stabilizes the desired posture trajectories. We discuss the advantage of the proposed strategy from two practical computational points of view: computational cost in the postural maintenance and redundancy resolution to suppress the internal motions. The implementation issues of the torque controller with hydraulic actuators are also discussed. The effectiveness of the proposed method is empirically validated by four kinds of full-body motion control experiments on our hydraulic biped anthropomorphic robot.IEEE/ASME Transactions on Mechatronics 01/2010; · 3.14 Impact Factor