A Unified Trajectory Optimization Approach for Long-Term and Reactive Motion Planning of Legged Locomotion

Shi, Yapeng; Yu, Bin; Ba, Kaixian; Li, Mantian

doi:10.1007/s42235-023-00362-w

Cited by 3 publications

(1 citation statement)

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“…Policies can output low-impedance joint position commands, which are very effective in many tasks. Such controllers outperform torque controllers regarding training speed and final control performance [51]. Although there is always a bi-directional mapping relationship between them, the smoothness of the two action parameterizations is different, and therefore, the training difficulty is different.…”

Section: Teacher Policymentioning

confidence: 99%

Dynamic Fall Recovery Control for Legged Robots via Reinforcement Learning

Li,

Pang,

Bai

et al. 2024

Biomimetics

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Falling is inevitable for legged robots when deployed in unstructured and unpredictable real-world scenarios, such as uneven terrain in the wild. Therefore, to recover dynamically from a fall without unintended termination of locomotion, the robot must possess the complex motor skills required for recovery maneuvers. However, this is exceptionally challenging for existing methods, since it involves multiple unspecified internal and external contacts. To go beyond the limitation of existing methods, we introduced a novel deep reinforcement learning framework to train a learning-based state estimator and a proprioceptive history policy for dynamic fall recovery under external disturbances. The proposed learning-based framework applies to different fall cases indoors and outdoors. Furthermore, we show that the learned fall recovery policies are hardware-feasible and can be implemented on real robots. The performance of the proposed approach is evaluated with extensive trials using a quadruped robot, which shows good effectiveness in recovering the robot after a fall on flat surfaces and grassland.

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Section: Teacher Policymentioning

confidence: 99%