[Paper] Difference-Aware Retrieval Policies for Imitation Learning
Source: arXiv - 2606.09758v1
Overview
Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusing the training data during inference via a semi-parametric retrieval-based imitation learning approach can alleviate this challenge. We present Difference-Aware Retrieval Policies for Imitation Learning (DARP), a semi-parametric retrieval-based imitation learning approach that addresses this limitation by reparameterizing the imitation learning problem in terms of local neighborhood structure rather than direct state-to-action mappings. Instead of learning a global policy, DARP trains a model to predict actions based on $k$-nearest neighbors from expert demonstrations, their corresponding actions, and the relative distance vectors between neighbor states and query states. DARP requires no additional assumptions beyond those made for standard behavior cloning — it does not require additional data collection, online expert feedback, or task-specific knowledge. We demonstrate consistent performance improvements of 15-46% over standard behavior cloning across diverse domains, including continuous control and robotic manipulation, and across different representations, including high-dimensional visual features. Code and demos are available at https://weirdlabuw.github.io/darp-site/.
Key Contributions
This paper presents research in the following areas:
- cs.RO
- cs.AI
- cs.LG
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.RO.
Authors
- Quinn Pfeifer
- Ethan Pronovost
- Paarth Shah
- Khimya Khetarpal
- Siddhartha Srinivasa
- Abhishek Gupta
Paper Information
- arXiv ID: 2606.09758v1
- Categories: cs.RO, cs.AI, cs.LG
- Published: June 8, 2026
- PDF: Download PDF