Baby Sophia: A Developmental Approach to Self-Exploration through Self-Touch and Hand Regard
This work addresses the challenge of curiosity-driven learning in robotics, though it is incremental as it builds on existing developmental approaches.
The researchers tackled the problem of enabling autonomous self-exploration in a robotic agent by developing a Reinforcement Learning framework inspired by infant development, resulting in the agent learning self-touch and hand regard behaviors through intrinsic rewards without external supervision.
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.