SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment
Caelan Garrett, Ajay Mandlekar, Bowen Wen, Dieter Fox

TL;DR
SkillMimicGen automates the creation of large, diverse demonstration datasets from minimal human input, significantly enhancing robot skill learning and enabling effective sim-to-real transfer for complex manipulation tasks.
Contribution
The paper introduces SkillMimicGen, a novel automated demonstration generation system that improves data efficiency and policy performance in robot manipulation tasks.
Findings
Generated over 24K demonstrations from 60 human demos
Achieved 24% higher success rates in simulation tasks
Enabled zero-shot sim-to-real transfer for long-horizon tasks
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTeaching and Learning Programming · Intelligent Tutoring Systems and Adaptive Learning
