Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning

Yenan Chen1; Chuye Zhang1; Pengxi Gu1; Jianuo Qiu1; Jiayi Yin2; Nuofan Qiu2; Guojing Huang1; Bangchao Huang1; Zishang Zhang1; Hui Deng1; Wei Zhang3; Fang Wan2,4; Chaoyang Song1,5

10.1109/ReMAR61031.2024.10618090

2024-06-26

979-8-3503-9597-6

2024 6th International Conference on Reconfigurable Mechanisms and Robots (ReMAR)

23-26 June 2024

Chicago, IL, USA

While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of ‘intelligent design under constraints’–hereafter referred to as constraint-driven design intelligence–in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4- bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.

第一

EI

1.Department of Mechanical and Energy Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China
2.SUSTech, School of Design
3.Department of System Design and Intelligent Manufacturing, SUSTech.
4.Shenzhen Key Lab of Intelligent Robotics & Flexible Manufacturing Systems, SUSTech.
5.SUSTech., SUSTech Institute of Robotics

Yenan Chen,Chuye Zhang,Pengxi Gu,et al. Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning[C],2024.