软体张拉整体机器人实现多行为物理计算，无需训练即可涌现新动作。
Multifunctional physical reservoir computing in soft tensegrity robots
- 利用软体机器人的非线性动力学特性进行无需训练的多任务计算。
- 系统在不同初始条件下收敛到不同吸引子，包含未训练的潜在行为模式。
- 揭示了具身智能中未被充分研究的认知机制，适合机器人与认知科学交叉研究者。
近期研究表明，在物理储层计算（PRC）框架下，物理系统的动态可被用于信息处理。具有柔性身体的机器人是此类物理系统的实例，其非线性体-环境动力学可用于生成控制自身行为所需的运动信号。本仿真研究将此方法扩展至一类称为张拉整体机器人（tensegrity robot）的软体机器人，使其不仅实现单一行为，还能嵌入多种行为。所形成的系统（机器人与环境）是一个多稳态动力系统，能从不同初始条件收敛至不同吸引子。吸引子分析表明，系统状态空间中存在超出训练数据范围的“未训练吸引子”，反映了张拉整体机器人及其与环境相互作用的内在特性与结构。这些发现对具身人工智能研究的影响尚未被充分探索，本文展示了它们在理解当前未充分解决的具身认知特征方面的潜力。
原文摘要 · Abstract (English)
Recent studies have demonstrated that the dynamics of physical systems can be utilized for the desired information processing under the framework of physical reservoir computing (PRC). Robots with soft bodies are examples of such physical systems, and their nonlinear body-environment dynamics can be used to compute and generate the motor signals necessary for the control of their own behavior. In this simulation study, we extend this approach to control and embed not only one but also multiple behaviors into a type of soft robot called a tensegrity robot. The resulting system, consisting of the robot and the environment, is a multistable dynamical system that converges to different attractors from varying initial conditions. Furthermore, attractor analysis reveals that there exist "untrained attractors" in the state space of the system outside the training data. These untrained attractors reflect the intrinsic properties and structures of the tensegrity robot and its interactions with the environment. The impacts of these recent findings in PRC remain unexplored in embodied AI research. We here illustrate their potential to understand various features of embodied cognition that have not been fully addressed to date.
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