Object-centric diffusion policies for real-world robotic-arm imitation learning

Imitation learning in complex, unstructured environments remains challenging due to the difficulty of grounding perception in physically meaningful representations and the need to model multimodal action distributions. Existing approaches often rely on unstructured pixel-level feature encodings or stochastic latent-variable decoders, which can lead to brittle attention in cluttered scenes. In this work, we present a novel integration of detector-based visual representations with conditional diff

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ethics.ai (15 July 2026), “Object-centric diffusion policies for real-world robotic-arm imitation learning,” evidence record 10687, https://ethics.ai/record/10687 (originally published by Frontiers in Robotics and AI).

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