Abstract
Living creatures and machines interact with the world through their morphology and motions. Recent advances in creating bio-inspired morphing robots and machines have led to the study of variable geometry truss (VGT), structures that can approximate arbitrary geometries and has large degree of freedom to deform. However, they are limited to simple geometries and motions due to the excessively complex control system. While a recent work PneuMesh solves this challenge with a novel VGT design that introduces a selective channel connection strategy, it imposes new challenge in identifying effective channel groupings and control methods. Building on top of the hardware concept presented in PneuMesh, we frame the challenge into a co-design problem and introduce a learning-based model to find a sub-optimal design. Specifically, given an initial truss structure provided by a human designer, we first adopt a genetic algorithm (GA) to optimize the channel grouping, and then couple GA with reinforcement learning (RL) for the control. The model is tailored to the PneuMesh system with customized initialization, mutation and selection functions, as well as the customized translation-invariant state vector for reinforcement learning. The result shows that our method enables a robotic table-based VGT to achieve various motions with a limited number of control inputs. The table is trained to move, lower its body or tilt its tabletop to accommodate multiple use cases such as benefiting kids and painters to use it in different shape states, allowing inclusive and adaptive design through morphing trusses.
Abstract (translated)
URL
https://arxiv.org/abs/2211.14663
