Instant Policy: In-Context Imitation Learning via Graph Diffusion

ICLR 2025 Conference Submission10684 Authors

27 Sept 2024 (modified: 17 Nov 2024)ICLR 2025 Conference SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Keywords: In-context Imitation Learning, Robotic Manipulation, Graph Neural Networks, Diffusion Models
TL;DR: We formulate In-Context Imitation Learning as a diffusion-based graph generation problem and learn it using procedurally generated pseudo-demonstrations.
Abstract: Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Instant Policy, which learns new tasks instantly from just one or two demonstrations, achieving ICIL through two key components. First, we introduce inductive biases through a graph representation and model ICIL as a graph generation problem using a learned diffusion process, enabling structured reasoning over demonstrations, observations, and actions. Second, we show that such a model can be trained using pseudo-demonstrations – arbitrary trajectories generated in simulation – as a virtually infinite pool of training data. Our experiments, in both simulation and reality, show that Instant Policy enables rapid learning of various everyday robot tasks. We also show how it can serve as a foundation for cross-embodiment and zero-shot transfer to language-defined tasks.
Supplementary Material: zip
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 10684
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