Blind Adaptive Gait Planning on Non-stationary Environments via Continual Reinforcement Learning

2021 ◽  
Author(s):  
Hao Hu ◽  
Yang Liu
Keyword(s):  
Reinforcement Learning ◽  
Gait Planning ◽  
Blind Adaptive ◽  
Adaptive Gait

Humanoid adaptive locomotion control through a bioinspired CPG-based controller

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Chenpeng Yao ◽  
Chengju Liu ◽  
Li Xia ◽  
Ming Liu ◽  
Qijun Chen
Keyword(s):  
Control Strategy ◽  
Sensory Feedback ◽  
Humanoid Robot ◽  
A Priori ◽  
Center Of Mass ◽  
Humanoid Robots ◽  
Pattern Generator ◽  
Gait Planning ◽  
Adaptive Gait ◽  
Walking Adaptability

Abstract To achieve adaptive gait planning of humanoid robots, a hierarchical central pattern generator (H-CPG) model with a basic rhythmic signal generation layer and a pattern formation layer is proposed to modulate the center of mass (CoM) and the online foot trajectory. The entrainment property of the CPG is exploited for adaptive walking in the absence of a priori knowledge of walking conditions, and the sensory feedback is applied to modulate the generated trajectories online to improve walking adaptability and stability. The developed control strategy is verified using a humanoid robot on sloped terrain and shows good performance.

Adaptive gait planning for multi-legged robots with an adjustment of center-of-gravity

Robotica ◽  
1999 ◽  
Vol 17 (4) ◽  
pp. 391-403 ◽  
Author(s):  
Wenjie Chen ◽  
K.H. Low ◽  
S.H. Yeo
Keyword(s):  
Control Systems ◽  
Stability Margin ◽  
Longitudinal Axis ◽  
Static Stability ◽  
Center Of Gravity ◽  
Legged Robots ◽  
Rough Terrain ◽  
Lateral Direction ◽  
Gait Planning ◽  
Adaptive Gait

Adaptive gait planning is an important aspect in the development of control systems for multi-legged robots traversing on rough terrain. The problem of adaptive gait generation can be viewed as one of finding a sequence of suitable foothold on rough terrain so that legged systems maintain static stability and motion continuity. Due to the limit of static stability, deadlock situation may occur in the process of searching for a suitable foothold, if terrain contains a large number of forbidden zones. In this paper, an improved method for adaptive gait planning is presented by active compensation of stability margin, through center of gravity (CG) adjustment in the longitudinal axis and/or body translation in the lateral direction. An algorithm for the proposed method is developed and embedded in a computer program. Simulation results show that the method provides legged machines with a much larger terrain adaptivity and better deadlock-avoidance ability.

scholarly journals Adaptive Gait Planning with Dynamic Movement Primitives for Walking Assistance Lower Exoskeleton in Uphill Slopes

2020 ◽  
Vol 32 (4) ◽  
pp. 1279
Author(s):  
Rui Huang ◽  
Qian Wu ◽  
Jing Qiu ◽  
Hong Cheng ◽  
Qiming Chen ◽  
...  
Keyword(s):  
Gait Planning ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Adaptive Gait ◽  
Dynamic Movement Primitives ◽  
Lower Exoskeleton

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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