Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation

TL;DR

This paper introduces Slot-Based Object-Centric Representations (SBOCR) for robotic manipulation, demonstrating that they improve generalization over traditional global and dense features under various visual changes.

Contribution

The work proposes SBOCR as an intermediate structured visual representation, showing its effectiveness in enhancing generalization in robotic manipulation tasks.

Findings

SBOCR outperforms dense and global features in generalization tests.

SBOCR maintains task performance under lighting, texture, and distractor variations.

No task-specific pretraining needed for SBOCR to be effective.

Abstract

The generalization capabilities of robotic manipulation policies are heavily influenced by the choice of visual representations. Existing approaches typically rely on representations extracted from pre-trained encoders, using two dominant types of features: global features, which summarize an entire image via a single pooled vector, and dense features, which preserve a patch-wise embedding from the final encoder layer. While widely used, both feature types mix task-relevant and irrelevant information, leading to poor generalization under distribution shifts, such as changes in lighting, textures, or the presence of distractors. In this work, we explore an intermediate structured alternative: Slot-Based Object-Centric Representations (SBOCR), which group dense features into a finite set of object-like entities. This representation permits to naturally reduce the noise provided to the robotic manipulation policy while keeping enough information to efficiently perform the task. We benchmark a range of global and dense representations against intermediate slot-based representations, across a suite of simulated and real-world manipulation tasks ranging from simple to complex. We evaluate their generalization under diverse visual conditions, including changes in lighting, texture, and the presence of distractors. Our findings reveal that SBOCR-based policies outperform dense and global representation-based policies in generalization settings, even without task-specific pretraining. These insights suggest that SBOCR is a promising direction for designing visual systems that generalize effectively in dynamic, real-world robotic environments.