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arXiv · 2605.13067

When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation

Maxime Alvarez·Ryo Watanabe·Paul Crook·Afshin Zeinaddini Meymand·Suvin Kurian·Pablo Ferreiro·Genki Sano
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Abstract

As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference conditions. Scaling the amount and diversity of the training data has shown some success in improving zero-shot generalization, yet robots still fail when faced with new, unseen test conditions. For instance, while robots with fixed frames of reference are common, those with moving frames pose a greater challenge for deployment. To address this specific instance of the issue, we present a study of strategies for encoding the robot's proprioceptive state to improve both in- and out-of-distribution performance at test time. Through a systematic study of joint representations, we find that a simple episode-wise relative frame provides the best trade-off between task performance and robustness, outperforming the baselines in extensive real-robot experiments conducted in a realistic test environment. The results suggest a practical path to leveraging data collected by robots with varying frames of reference and deployment to unseen test configurations.

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Maxime Alvarez, Ryo Watanabe, Paul Crook, Afshin Zeinaddini Meymand, Suvin Kurian, Pablo Ferreiro, Genki Sano. 2026-05-13. When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation. https://arxiv.org/abs/2605.13067

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