# When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation

arXiv: https://arxiv.org/abs/2605.13067 · HTML: https://mlsanity.com/paper/2605.13067/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 19 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 16 of 16)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://mlsanity.com/paper/2210.11339/) (2022)
- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/2211.09325/) (2022)
- [The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments](https://mlsanity.com/paper/2109.10892/) (2021)
- [ZeroMimic: Distilling Robotic Manipulation Skills from Web Videos](https://mlsanity.com/paper/2503.23877/) (2025)
- [TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning](https://mlsanity.com/paper/2412.10447/) (2024)
- [Learning Task Space Actions for Bipedal Locomotion](https://mlsanity.com/paper/2011.04741/) (2020)
- [Do You Need Proprioceptive States in Visuomotor Policies?](https://mlsanity.com/paper/2509.18644/) (2025)
- [When would Vision-Proprioception Policies Fail in Robotic Manipulation?](https://mlsanity.com/paper/2602.12032/) (2026)
- [AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation](https://mlsanity.com/paper/2509.25032/) (2025)
- [FTACT: Force Torque aware Action Chunking Transformer for Pick-and-Reorient Bottle Task](https://mlsanity.com/paper/2509.23112/) (2025)

## Built on by (most cited first, 1 of 1)

- [Policy-Induced Hand Priors in Humanoid Dual-Arm Manipulation: Diagnosing and Mitigating Initial-Pose Dependence](https://mlsanity.com/paper/2608.11769/) (2026)

## Abstract (arXiv)

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.