机器人自主学习感官与动作关联，实时更新身体模型。
GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
- 通过自身经验学习传感器与执行器的关联结构。
- 支持工具使用、肌肉关节映射和全身操作，适应变化环境。
- 可实现状态估计、异常检测与仿真，适用于多种机器人形态。
人类能自主学习自身身体中感觉与运动的关系，估测并控制身体状态，并在持续适应环境中行动。而当前机器人依赖人工设计的网络结构进行控制，对传感器与执行器的关系做出预设假设，且模型无法随机器人本体、所握工具或环境变化而自适应，缺乏统一理论支撑控制、状态估计、异常检测、仿真等任务。本文提出广义多感官相关模型（GeMuCo），使机器人通过自身经验学习传感器与执行器间的关联关系，包括网络输入输出结构。机器人可在线更新该身体模型以适应当前环境，实现身体状态估计与控制，并支持异常检测与仿真。我们在轴驱动机器人上验证了其在抓握状态变化下的工具使用能力，在肌骨骼机器人上实现了关节-肌肉映射学习，并在低刚度塑料人形机器人上完成了全身工具操作，证明了方法的有效性。
原文摘要 · Abstract (English)
Humans can autonomously learn the relationship between sensation and motion in their own bodies, estimate and control their own body states, and move while continuously adapting to the current environment. On the other hand, current robots control their bodies by learning the network structure described by humans from their experiences, making certain assumptions on the relationship between sensors and actuators. In addition, the network model does not adapt to changes in the robot's body, the tools that are grasped, or the environment, and there is no unified theory, not only for control but also for state estimation, anomaly detection, simulation, and so on. In this study, we propose a Generalized Multisensory Correlational Model (GeMuCo), in which the robot itself acquires a body schema describing the correlation between sensors and actuators from its own experience, including model structures such as network input/output. The robot adapts to the current environment by updating this body schema model online, estimates and controls its body state, and even performs anomaly detection and simulation. We demonstrate the effectiveness of this method by applying it to tool-use considering changes in grasping state for an axis-driven robot, to joint-muscle mapping learning for a musculoskeletal robot, and to full-body tool manipulation for a low-rigidity plastic-made humanoid.
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