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arXiv · 2610.39429

ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation Learning

Neel Jawale·Stephan Pleines·Soha Pouya·Yuke Zhu·Ajay Mandlekar·Caelan Garrett
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Abstract

Multi-arm robot platforms, including bimanual and humanoid robots, have the capability to complete tasks with lower cycle times than single-arm systems, but they are more difficult to safely control due to the presence of multi-arm constraints. The paradigm of training generalist robot policies from massive datasets is a promising approach to address general-purpose multi-arm manipulation, but the acquisition of large multi-arm manipulation datasets remains a bottleneck. Automated demonstration generation can alleviate this burden by synthesizing new demonstrations from a handful of source demonstrations, but existing algorithms ignore multi-arm constraints and as a result often induce unsafe and unsuccessful behaviors. We address this limitation by introducing ScheduleGen, an integrated planning & scheduling algorithm for multi-arm demonstration generation. We evaluate ScheduleGen on 28 simulation task variants, including seven externally authored RoboDojo tasks and settings inaccessible to prior systems; it achieves 79.6% mean data-generation success and trains policies with 65.5% mean success. ScheduleGen policies outperform those trained on a state-of-the-art data generation baseline, and achieve 50-80% success across eight real-world variants through zero-shot sim-to-real and real-to-sim-to-real transfer. Videos and more at: https://schedulegen.github.io/

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Neel Jawale, Stephan Pleines, Soha Pouya, Yuke Zhu, Ajay Mandlekar, Caelan Garrett. 2026-10-29. ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation Learning. https://arxiv.org/abs/2610.39429

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