Flexible Adaptive Control of Snake-Like Robot Based on LSTM and Gait

Ouyang, Sheng; Wu, Wei

doi:10.1088/1742-6596/1487/1/012049

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, all these works were confined by the parameterized gait-generation system and have a very limited effect on further improving the energy efficiency of a gait. Moreover, this open-loop method is also time consuming and inefficient [10] for fine-tuning gait parameters to achieve expected control objectives.…”

Section: Gait Optimization With Model-based Methodsmentioning

confidence: 99%

Simulation to Real: Learning Energy-Efficient Slithering Gaits for a Snake-Like Robot

Bing

Cheng

Huang

et al. 2022

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

o resemble the body flexibility of biological snakes, snake-like robots are designed as a chain of body modules, which gives them many degrees of freedom (DoF) on the one hand and leads to a challenging task to control them on the other. Compared with conventional model-based control methods, reinforcement learning (RL)-based ones provide promising solutions to design agile and energy-efficient gaits for snakelike robots as RL-based methods can fully exploit the hyperredundant bodies of the robots. However, RL-based methods for snake-like robots have rarely been investigated even in simulations, let alone been deployed on real-world snake-like robots. In this work, we introduce a novel approach for designing energy-efficient gaits for a snake-like robot, which first learns a policy using an RL algorithm in simulation and then transfers it to the real-world testing, thereby leveraging a fast and economical gait-generation process. We evaluate our RL-based approach in both simulations and real-world experiments to demonstrate that it can generate substantially more energy-efficient gaits than those generated by conventional model-based controllers.

show abstract

Section: Gait Optimization With Model-based Methodsmentioning

confidence: 99%