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SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment

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arxiv 2410.18907 v1 pith:FAFR7ZVT submitted 2024-10-24 cs.RO cs.AIcs.CVcs.LG

Caelan Garrett , Ajay Mandlekar , Bowen Wen , Dieter Fox This is my paper · ORCID
classification cs.ROcs.AIcs.CVcs.LG
keywords skillgenhumanlearningdatadatasetsdemonstratedemonstrationsgeneration
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Imitation learning from human demonstrations is an effective paradigm for robot manipulation, but acquiring large datasets is costly and resource-intensive, especially for long-horizon tasks. To address this issue, we propose SkillMimicGen (SkillGen), an automated system for generating demonstration datasets from a few human demos. SkillGen segments human demos into manipulation skills, adapts these skills to new contexts, and stitches them together through free-space transit and transfer motion. We also propose a Hybrid Skill Policy (HSP) framework for learning skill initiation, control, and termination components from SkillGen datasets, enabling skills to be sequenced using motion planning at test-time. We demonstrate that SkillGen greatly improves data generation and policy learning performance over a state-of-the-art data generation framework, resulting in the capability to produce data for large scene variations, including clutter, and agents that are on average 24% more successful. We demonstrate the efficacy of SkillGen by generating over 24K demonstrations across 18 task variants in simulation from just 60 human demonstrations, and training proficient, often near-perfect, HSP agents. Finally, we apply SkillGen to 3 real-world manipulation tasks and also demonstrate zero-shot sim-to-real transfer on a long-horizon assembly task. Videos, and more at https://skillgen.github.io.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    SIDO morphs static demonstrations into counterfactual future-pose samples, training a goal-conditioned policy that, paired with a pose predictor, grasps objects whose motion was unseen during training.

  2. SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    SimFoundry automates zero-shot real-to-sim scene generation from video, producing digital twins and cousins that enable policy training with 0.911 mean Pearson correlation to real-world results and 17-40% success gain...

  3. LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs

    cs.RO 2025-09 conditional novelty 6.0 of 10

    An LLM-based pipeline automatically augments one human demonstration into a large imitation-learning dataset, using Thompson sampling to pick the best annotation and beating expert-annotated baselines on most tasks.

  4. Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)

    cs.RO 2025-09 conditional novelty 6.0 of 10

    A robot policy trained on one real demonstration plus AI-generated 3D views succeeds from novel initial poses, including opposite-side starts, across six real manipulation tasks.

  5. LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations

    cs.RO 2025-08 conditional novelty 6.0 of 10

    LodeStar combines automatic skill segmentation with simulation-based reinforcement learning augmentation and a learned routing transformer to let a robotic hand complete long-horizon dexterous tasks from a few human demos.

  6. SViP: Sequencing Bimanual Visuomotor Policies with Object-Centric Motion Primitives

    cs.RO 2025-06 conditional novelty 6.0 of 10

    SViP shows that sequencing a visuomotor policy with TAMP-planned primitives, via learned switching conditions and feasibility checks, improves generalization from few demonstrations in bimanual manipulation.

  7. SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training

    cs.RO 2025-07 conditional novelty 5.0 of 10

    Simulation-pretrained policies, with digital-twin demos for critic bootstrapping and action proposals, cut real-world RL training time while reaching near-perfect success on three manipulation tasks.

  8. RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning

    cs.RO 2025-04 conditional novelty 5.0 of 10

    RoboVerse unifies seven simulators, 15 benchmarks, and 510.5k migrated trajectories into one platform with a four-level generalization benchmark, claiming better robot learning and sim-to-real transfer.

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