机器人模仿婴儿发育，靠好奇自学摸手和看手
Baby Sophia: A Developmental Approach to Self-Exploration through Self-Touch and Hand Regard
- 用内在奖励模拟婴儿好奇，驱动机器人自主探索身体
- 无需外部监督，实现触觉与视觉的协同学习
- 适合研究具身智能和发育学习的科研人员
受婴儿发育启发，我们提出一种强化学习框架，用于在机器人婴儿索菲亚（Baby Sophia）中实现自主自我探索，基于BabyBench仿真环境。该代理通过模拟婴儿好奇心的内在奖励，学习自触觉和手注视行为。对于自触觉，高维触觉输入被转化为紧凑有意义的表征，以支持高效学习；通过内在奖励与课程学习，鼓励覆盖全身、保持平衡并实现泛化。对于手注视，通过运动试错学习手部视觉特征（如肤色、形状），再以内在奖励推动执行新颖手部动作，并引导视线追随双手。从单手到双手的课程学习设计，使代理达成复杂的视听-运动协调。结果表明，仅凭纯好奇心信号，无需外部监督，即可驱动多模态协调学习，类比婴儿从随机运动尝试到有目的行为的发展过程。
原文摘要 · Abstract (English)
Inspired by infant development, we propose a Reinforcement Learning (RL) framework for autonomous self-exploration in a robotic agent, Baby Sophia, using the BabyBench simulation environment. The agent learns self-touch and hand regard behaviors through intrinsic rewards that mimic an infant's curiosity-driven exploration of its own body. For self-touch, high-dimensional tactile inputs are transformed into compact, meaningful representations, enabling efficient learning. The agent then discovers new tactile contacts through intrinsic rewards and curriculum learning that encourage broad body coverage, balance, and generalization. For hand regard, visual features of the hands, such as skin-color and shape, are learned through motor babbling. Then, intrinsic rewards encourage the agent to perform novel hand motions, and follow its hands with its gaze. A curriculum learning setup from single-hand to dual-hand training allows the agent to reach complex visual-motor coordination. The results of this work demonstrate that purely curiosity-based signals, with no external supervision, can drive coordinated multimodal learning, imitating an infant's progression from random motor babbling to purposeful behaviors.
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