Baby Sophia: A Developmental Approach to Self-Exploration through Self-Touch and Hand Regard

TL;DR

This paper presents a reinforcement learning framework for a robot, Baby Sophia, to autonomously learn self-touch and hand regard behaviors through intrinsic rewards, mimicking infant development and curiosity-driven exploration.

Contribution

It introduces a novel RL approach that enables a robot to learn self-exploration behaviors using intrinsic motivation without external supervision.

Findings

Robot learns self-touch behaviors through tactile input processing.

Agent develops visual-motor coordination for hand regard tasks.

Curiosity-driven learning leads to purposeful behaviors from random exploration.

Abstract

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.