Disentangled Object-Centric Image Representation for Robotic Manipulation

• URL: http://arxiv.org/abs/2503.11565v1
• Date: Fri, 14 Mar 2025 16:33:48 GMT
• Title: Disentangled Object-Centric Image Representation for Robotic Manipulation
• Authors: David Emukpere, Romain Deffayet, Bingbing Wu, Romain Brégier, Michael Niemaz, Jean-Luc Meunier, Denys Proux, Jean-Michel Renders, Seungsu Kim,
• Abstract summary: We propose DOCIR, an object-centric framework that introduces a disentangled representation for objects of interest, obstacles, and robot embodiment.<n>We show that this approach leads to state-of-the-art performance for learning pick and place skills from visual inputs in multi-object environments.
• Score: 6.775909411692767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning robotic manipulation skills from vision is a promising approach for developing robotics applications that can generalize broadly to real-world scenarios. As such, many approaches to enable this vision have been explored with fruitful results. Particularly, object-centric representation methods have been shown to provide better inductive biases for skill learning, leading to improved performance and generalization. Nonetheless, we show that object-centric methods can struggle to learn simple manipulation skills in multi-object environments. Thus, we propose DOCIR, an object-centric framework that introduces a disentangled representation for objects of interest, obstacles, and robot embodiment. We show that this approach leads to state-of-the-art performance for learning pick and place skills from visual inputs in multi-object environments and generalizes at test time to changing objects of interest and distractors in the scene. Furthermore, we show its efficacy both in simulation and zero-shot transfer to the real world.

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