Bio-Inspired Phase-Aware Skill Graph for Robust Long-Horizon Robotic Manipulation with Promptable Control

Abstract

Long-horizon robotic manipulation requires coordinating multiple motor primitives under uncertainty, especially in contact-rich and changing environments. Existing end-to-end visuomotor policies often lack explicit temporal structure, causing brittle execution and limited recovery after disturbances. Inspired by biological motor control, where reusable primitives are organized through phase decomposition and feedback-dependent transitions, we propose a bio-inspired phase-aware framework that represents execution as structured transitions over perception-grounded motor phases. The framework integrates three modules: a Multimodal Phase-and-Primitive Detector that extracts semantically and physically consistent phases from visual, proprioceptive, and force–torque signals; a Multimodal Perception Skill Graph (MPSG) that encodes feasible phase transitions and supports skipping, rollback, and recovery; and Promptable Phase Control, which converts language instructions into graph-level ordering constraints for task reordering without retraining low-level policies. Experiments on four multi-stage tasks show improved robustness, increasing the average disturbed-condition success rate from 25.0% to 73.8% relative to the monolithic Action Chunking with Transformers (ACT) baseline.