14 research outputs found
Push Recovery of a Position-Controlled Humanoid Robot Based on Capture Point Feedback Control
In this paper, a combination of ankle and hip strategy is used for push
recovery of a position-controlled humanoid robot. Ankle strategy and hip
strategy are equivalent to Center of Pressure (CoP) and Centroidal Moment Pivot
(CMP) regulation respectively. For controlling the CMP and CoP we need a
torque-controlled robot, however most of the conventional humanoid robots are
position controlled. In this regard, we present an efficient way for
implementation of the hip and ankle strategies on a position controlled
humanoid robot. We employ a feedback controller to compensate the capture point
error. Using our scheme, a simple and practical push recovery controller is
designed which can be implemented on the most of the conventional humanoid
robots without the need for torque sensors. The effectiveness of the proposed
approach is verified through push recovery experiments on SURENA-Mini humanoid
robot under severe pushes
Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot
One of the challenges for the robotics community is to deploy robots which
can reliably operate in real world scenarios together with humans. A crucial
requirement for legged robots is the capability to properly balance on their
feet, rejecting external disturbances. iCub is a state-of-the-art humanoid
robot which has only recently started to balance on its feet. While the current
balancing controller has proved successful in various scenarios, it still
misses the capability to properly react to strong pushes by taking steps. This
paper goes in this direction. It proposes and implements a control strategy
based on the Capture Point concept [1]. Instead of relying on position control,
like most of Capture Point related approaches, the proposed strategy generates
references for the momentum-based torque controller already implemented on the
iCub, thus extending its capabilities to react to external disturbances, while
retaining the advantages of torque control when interacting with the
environment. Experiments in the Gazebo simulator and on the iCub humanoid robot
validate the proposed strategy
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Benchmarking Stability of Bipedal Locomotion Based on Individual Full Body Dynamics and Foot Placement Strategies–Application to Impaired and Unimpaired Walking
The principles underlying smooth and effortless human walking while maintaining stability as well as the ability to quickly respond to unexpected perturbations result from a plethora of well-balanced parameters, most of them yet to be determined. In this paper, we investigate criteria that may be useful for benchmarking stability properties of human walking. We perform dynamic reconstructions of human walking motions of unimpaired subjects and subjects walking with transfemoral prostheses from motion capture recordings using optimal control. We aim at revealing subject-specific strategies in applying dynamics in order to maintain steady gait considering irregularities such as deviating gait patterns or asymmetric body segment properties. We identify foot placement with respect to the Instantaneous Capture Point as the strategy globally applied by the subjects to obtain steady gait and propose the Residual Orbital Energy as a measure allowing for benchmarking human-like gait toward confident vs. cautious gait