SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment (2410.18907v1)

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 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.

• The paper presents SkillMimicGen, a novel approach that automates segmentation and generation of demonstration datasets for robotic imitation learning.
• The methodology leverages a Hybrid Skill Policy to combine human demonstration segmentation with motion planning, enhancing policy learning performance.
• SkillMimicGen scales from 60 to over 24,000 simulated demonstrations, achieving a 24% improvement in policy success compared to existing frameworks.

An Analysis of SkiLLMimicGen: Automated Demonstration Generation for Robotic Skill Learning

The paper "SkiLLMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deployment" introduces SkillGen, a novel approach aimed at streamlining the generation of demonstration datasets for imitation learning in robotic manipulation. Traditional imitation learning methods rely heavily on large datasets of human demonstrations, which are expensive and time-consuming to collect, particularly for complex, long-horizon tasks. SkillGen addresses this challenge by efficiently generating demonstration datasets from a minimal set of human demonstrations, advancing both data generation efficiency and policy learning performance.

Methodology and Framework

SkillGen introduces an automated procedure to segment human demonstrations into distinct manipulation skills, adapting these skills to novel contexts and stitching them together with motion planning techniques. This segmentation significantly reduces the reliance on continuous human intervention by automating the replay of short skill segments with traditional free-space motion planning, thereby improving demonstration quality and policy learning outcomes.

A key innovation is the Hybrid Skill Policy (HSP) framework, which allows the system to learn the initiation, control, and termination components of these skills. By utilizing motion planning to sequence skills during test time, SkillGen maintains robust performance across various scene configurations.

Numerical Results and Claims

The paper reports significant improvements in data generation success rates and agent performance. Specifically, SkillGen demonstrates its ability to scale up from 60 human demonstrations to over 24,000 simulation demonstrations across 18 task variants, achieving an average increase of 24% in policy success over state-of-the-art frameworks like MimicGen.

In addition, SkillGen shows impressive versatility and robustness in handling large scene variations, including cluttered environments. The paper validates SkillGen through real-world applications, achieving successful zero-shot sim-to-real transfers on long-horizon tasks.

Implications and Future Directions

The SkillGen framework significantly reduces the overhead of data collection for robotic imitation learning, increasing feasibility for real-world applications. The decomposition into manipulation skills and transit motion segments aligns well with the capabilities of modern robotic systems, offering a practical solution for efficient policy training.

Potential future work could explore expanding SkillGen's applicability beyond quasi-static tasks to dynamic environments and extending the framework to handle more diverse object types. Additionally, the integration of more sophisticated modeling techniques could enhance SkillGen's capability to adapt to even subtler variations in task parameters.

Conclusion

SkiLLMimicGen represents a forward-thinking advancement in the field of robotic skill learning, offering a scalable solution to the challenges associated with demonstration dataset generation and policy learning. The paper delivers substantial evidence of its efficacy in both simulated and real-world environments, providing a robust foundation for ongoing research and application in advanced robotic systems.