COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning

Sateesh Kumar, Shivin Dass, Georgios Pavlakos, Roberto Martín-Martín

Keywords: Imitation Learning, Few-Shot Learning, Data Retrieval

TL;DR: COLLAGE is a few-shot imitation learning method that retrieves and fuses subsets from large datasets using multiple similarity measures. Each subset is scored by training a policy, and these scores are used to guide data sampling during training.

Abstract:

In this work, we study the problem of data retrieval for few-shot imitation learning: select data from a large dataset to train a performant policy for a specific task, given only a few target demonstrations. Prior methods retrieve data using a single-feature distance heuristic, assuming that the best demonstrations are those that most closely resemble the target examples in visual, semantic, or motion
space. However, this approach captures only a subset of the relevant information and is prone to introducing detrimental demonstrations, e.g., retrieving data from unrelated tasks due to similar scene layouts, or selecting similar motions from tasks with divergent goals. We present COLLAGE, a method for COLLective data AGgrEgation in few-shot imitation learning that uses an adaptive late fusion mechanism to guide the selection of relevant demonstrations based on a task-specific combination of multiple cues. COLLAGE follows a simple, but flexible and efficient data aggregation recipe: it assigns weights to subsets of the dataset that are pre-selected using a single feature (e.g., appearance, shape, or language similarity), based on their task relevance, measured by how well a policy trained on each subset predicts actions in the few target demonstrations. These weights are then used during policy training to perform importance sampling over the aggregated dataset, sampling data more densely or sparsely, according to their estimated relevance. This weighted aggregation strategy is general and feature-agnostic, allowing COLLAGE to combine and leverage any number of subsets selected by any retrieval heuristic or method, and to identify which subset provides the most benefit for the target task. In extensive experiments, COLLAGE outperforms state-of-the-art retrieval and multi-task learning approaches, achieving a 5.1% improvement over the best baseline in simulation across 10 tasks, and a 16.6% improvement in the real world across 6 tasks. For our real world experiments, we include data selection from the large-scale, real-world DROID dataset, significantly improving few-shot imitation policy training. More information at: https://collagecorl25.github.io/

Submission Number: 6