Inspired by animals that co-adapt their brain and body to interact with the environment, we present a tendon-driven and over-actuated (i.e., n joint, n+1 actuators) bipedal robot that (i) exploits its backdrivable mechanical properties to manage body-environment interactions without explicit control, and (ii) uses a simple 3-layer neural network to learn to walk after only 2 minutes of 'natural' motor babbling (i.e., an exploration strategy that is compatible with leg and task dynamics; akin to childsplay). This brain-body collaboration first learns to produce feet cyclical movements 'in air' and, without further tuning, can produce locomotion when the biped is lowered to be in slight contact with the ground. In contrast, training with 2 minutes of 'naive' motor babbling (i.e., an exploration strategy that ignores leg task dynamics), does not produce consistent cyclical movements 'in air', and produces erratic movements and no locomotion when in slight contact with the ground. When further lowering the biped and making the desired leg trajectories reach 1cm below ground (causing the desired-vs-obtained trajectories error to be unavoidable), cyclical movements based on either natural or naive babbling presented almost equally persistent trends, and locomotion emerged with naive babbling. Therefore, we show how continual learning of walking in unforeseen circumstances can be driven by continual physical adaptation rooted in the backdrivable properties of the plant and enhanced by exploration strategies that exploit plant dynamics. Our studies also demonstrate that the bio-inspired codesign and co-adaptations of limbs and control strategies can produce locomotion without explicit control of trajectory errors.

翻译：受动物通过协同适应大脑与身体以与环境互动的启发，我们提出一种肌腱驱动、过驱动（即n关节、n+1驱动器）的双足机器人。该机器人（i）利用其反向驱动机械特性管理身体-环境交互而无需显式控制，且（ii）仅通过2分钟“自然”运动咿呀学语（即与腿部及任务动态兼容的探索策略，类似儿童玩耍）后，即可使用简单三层神经网络学习行走。这种脑-体协作首先学会“在空中”产生足部周期性运动，随后无需额外调整，当双足机器人降至与地面轻微接触时即可产生行进能力。相比之下，采用2分钟“朴素”运动咿呀学语（即忽略腿部任务动态的探索策略）训练，无法产生“在空中”的稳定周期性运动，且与地面轻微接触时运动紊乱且无法行进。进一步降低双足机器人高度，使期望腿部轨迹深入地面1厘米（导致期望-实际轨迹误差不可避免）时，基于自然或朴素咿呀学语产生的周期性运动表现出近乎等同的持续性，且朴素咿呀学语亦可产生行进能力。因此，我们展示了在不可预见情境下持续学习行走的能力，可源于根植于机体反向驱动特性的持续物理适应，并通过利用机体动态的探索策略得以增强。本研究还表明，仿生协同设计与肢体及控制策略的协同适应，可在无需显式轨迹误差控制的情况下产生行进能力。