Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
This addresses the problem of complex robot coordination for researchers in robotics and AI, representing an incremental improvement over existing methods.
The paper tackles the challenge of long-horizon contact-rich bimanual manipulation by introducing a hierarchical framework that integrates skill planning and scheduling, achieving higher success rates than end-to-end RL approaches and more efficient behaviors than sequential-only planners.
Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaboration between arms. In this paper, we introduce a hierarchical framework that frames this challenge as an integrated skill planning & scheduling problem, going beyond purely sequential decision-making to support simultaneous skill invocation. Our approach is built upon a library of single-arm and bimanual primitive skills, each trained using Reinforcement Learning (RL) in GPU-accelerated simulation. We then train a Transformer-based planner on a dataset of skill compositions to act as a high-level scheduler, simultaneously predicting the discrete schedule of skills as well as their continuous parameters. We demonstrate that our method achieves higher success rates on complex, contact-rich tasks than end-to-end RL approaches and produces more efficient, coordinated behaviors than traditional sequential-only planners.