Progressive Policy Learning: A Hierarchical Framework for Dexterous Bimanual Manipulation
Year: 2025
Type: article
Abstract: Dexterous bimanual manipulation remains a challenging task in reinforcement learning (RL) due to the vast state–action space and the complex interdependence between the hands. Conventional end-to-end learning struggles to handle this complexity, and multi-agent RL often faces limitations in stably acquiring cooperative movements. To address these issues, this study proposes a hierarchical progressive policy learning framework for dexterous bimanual manipulation. In the proposed method, one hand’s policy is first trained to stably grasp the object, and, while maintaining this grasp, the other hand’s manipulation policy is progressively learned. This hierarchical decomposition reduces the search space for each policy and enhances both the connectivity and the stability of learning by training the subsequent policy on the stable states generated by the preceding policy. Simulation results show that the proposed framework outperforms conventional end-to-end and multi-agent RL approaches. The proposed method was demonstrated via sim-to-real transfer on a physical dual-arm platform and empirically validated on a bimanual cube manipulation task.
Source: Mathematics
DOI: 10.3390/math13223585
Institution: Dongguk University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4416092487