ScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitat
Overview
This paper presents ScheduleGen, an integrated planning and scheduling algorithm for generating safe demonstrations for multi-arm robots. Evaluated on 28 simulation tasks, it achieves 79.6% mean data-generation success and outperforms state-of-the-art baselines in real-world transfer with 50-80% success across eight variants.
From arXiv Robotics — research abstracts
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“grated 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”
Exact source · revision 1Source owner not reported
Reported quantity · simulation task variants: 28 other · Basis not reported
“grated 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”
Exact source · revision 1Source owner not reported
Reported quantity · real-world variants: 8 other · Basis not reported
“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/”
Exact source · revision 1Source owner not reported
Reported quantity · RoboDojo tasks: 7 other · Basis not reported
“grated 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”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-10-30T23:49:04.778Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalScheduleGen: Demonstration Generation via Skill Scheduling for Multi-Arm Imitation Learning
This paper presents ScheduleGen, an integrated planning and scheduling algorithm for generating safe demonstrations for multi-arm robots. Evaluated on 28 simulation tasks, it achieves 79.6% mean data-generation success and outperforms state-of-the-art baselines in real-world transfer with 50-80% success across eight variants.
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