SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
CVPR 2024(2023)
摘要
Diffusion models have demonstrated strong potential for robotic trajectory
planning. However, generating coherent trajectories from high-level
instructions remains challenging, especially for long-range composition tasks
requiring multiple sequential skills. We propose SkillDiffuser, an end-to-end
hierarchical planning framework integrating interpretable skill learning with
conditional diffusion planning to address this problem. At the higher level,
the skill abstraction module learns discrete, human-understandable skill
representations from visual observations and language instructions. These
learned skill embeddings are then used to condition the diffusion model to
generate customized latent trajectories aligned with the skills. This allows
generating diverse state trajectories that adhere to the learnable skills. By
integrating skill learning with conditional trajectory generation,
SkillDiffuser produces coherent behavior following abstract instructions across
diverse tasks. Experiments on multi-task robotic manipulation benchmarks like
Meta-World and LOReL demonstrate state-of-the-art performance and
human-interpretable skill representations from SkillDiffuser. More
visualization results and information could be found on our website.
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