REVIEW 4 cited by
Instant Policy: In-Context Imitation Learning via Graph Diffusion
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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 (without further training) 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 with 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. Simulated and real experiments 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. Code and videos are available at https://www.robot-learning.uk/instant-policy.
Forward citations
Cited by 4 Pith papers
-
MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos
Trained only on unlabeled human play videos, MimicDroid lets a GR1 humanoid perform new manipulation tasks from one to three demonstration videos, with roughly twice the real-world success of prior video-conditioned methods.
-
Try Once, Then Optimal: De-Redundified Procedure Memory for Cross-Episode Exploration Amortization
Object-centric procedure memory amortizes hidden-state exploration across encounters, cutting robot manipulation operations 16–30% at non-regressing success.
-
Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
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.
-
Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions
A semantic keypoint graph matched to novel objects lets imitation-learned manipulation policies generalize with a quarter of the demonstrations.
Discussion (0). Sign in to comment.