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.

本研究基于 PneuMesh 硬件构想，设计了一个学习模型和遗传算法和强化学习相结合的优化方案，以帮助机械桌在少数控制输入的情况下实现各种运动，包括变形桁架的包容性和适应性设计。