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arxiv: 2605.13067 · v1 · pith:Z6ML7U34new · submitted 2026-05-13 · 💻 cs.RO · cs.AI

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 This is my paper

Pith reviewed 2026-05-14 19:03 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords proprioceptive encodingsrobotic manipulationrelative framesrobustnessgeneralizationreal-robot experimentsstate representation
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The pith

A simple episode-wise relative frame for proprioceptive encoding delivers better performance and robustness than absolute state representations in real robotic manipulation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper studies how to represent a robot's own joint positions and movements so that learned policies work well even when the robot's position or orientation changes between training and testing. It compares several encoding strategies and finds that resetting the reference frame at the start of each episode gives the strongest combination of accurate task completion and resistance to new conditions. Experiments on real robots in varied setups confirm this simple method beats common absolute encodings and other relative approaches. The results open a straightforward route to training on mixed data from different robot frames and deploying without adjustment.

Core claim

An episode-wise relative frame, in which proprioceptive observations are expressed relative to the configuration at the start of the current episode, yields superior task success and robustness to frame shifts compared with absolute joint states or other relative schemes.

What carries the argument

Episode-wise relative proprioceptive encoding, which normalizes joint angles and velocities against the initial pose of each episode to remove dependence on the absolute reference frame.

If this is right

  • Training data collected across robots with different base positions can be combined effectively using this encoding.
  • Deployment in environments where the robot base moves or is placed differently becomes feasible without policy retraining.
  • Real-robot performance improves in realistic test conditions that include frame variations.
  • Simpler encodings can outperform more complex learned representations for proprioception in this setting.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Applying the same relative reset idea to other state variables such as camera poses might further reduce sensitivity to setup changes.
  • Tasks with long horizons could benefit from periodic re-zeroing of the relative frame rather than a single episode start.
  • The finding highlights that absolute coordinate systems in state spaces are often a hidden source of brittleness in deployed policies.

Load-bearing premise

The test tasks and environment variations adequately represent the kinds of frame changes that occur in actual deployments.

What would settle it

Running the same policies on a robot whose base is translated or rotated by an amount larger than any variation tested in the paper, and measuring whether the episode-wise relative method still outperforms the absolute baseline.

Figures

Figures reproduced from arXiv: 2605.13067 by Afshin Zeinaddini Meymand, Genki Sano, Maxime Alvarez, Pablo Ferreiro, Paul Crook, Ryo Watanabe, Suvin Kurian.

Figure 1
Figure 1. Figure 1: Comparison of the three state-action representations. Absolute [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the proposed episode-wise relative state and actions [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Bottle-recovery task decomposed into four stages: (a) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the starting values for each episode in the dataset [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: X and Z values for 5 random episodes from the training dataset, [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper evaluates proprioceptive state encodings for end-to-end robotic manipulation policies to address performance degradation when the robot's frame of reference changes between training and test time. Through a systematic comparison, it claims that a simple episode-wise relative encoding achieves the best trade-off, outperforming absolute-state and other baselines in both in-distribution and out-of-distribution settings, as demonstrated by real-robot experiments in a realistic test environment.

Significance. If the experimental results hold under scrutiny, the finding offers a lightweight, data-efficient approach to improving policy robustness to frame variations without architectural changes or additional data collection, which could meaningfully aid deployment of manipulation policies in unstructured real-world settings where absolute frames are impractical.

major comments (2)
  1. [§4] §4 (Real-robot experiments): The central claim of outperformance rests on real-robot trials, yet the text provides no quantitative metrics (e.g., success rates, trajectory errors), number of trials, statistical tests, or implementation details for baselines, rendering the reported superiority unverifiable and the robustness conclusion unsupported.
  2. [§4.3] §4.3 (Test variations): The evaluation uses only discrete frame shifts in a fixed test environment; no experiments address continuous drifts, compounding errors, or multi-axis movements, so the claim that the encoding generalizes to broader real-world frame changes lacks direct evidence.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'extensive real-robot experiments' would be strengthened by a parenthetical note on the number of tasks or trials performed.
  2. [§3.2] Notation: The distinction between 'episode-wise relative frame' and other joint representations could be clarified with a short equation or pseudocode in §3.2.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the real-robot experiments. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§4] §4 (Real-robot experiments): The central claim of outperformance rests on real-robot trials, yet the text provides no quantitative metrics (e.g., success rates, trajectory errors), number of trials, statistical tests, or implementation details for baselines, rendering the reported superiority unverifiable and the robustness conclusion unsupported.

    Authors: We agree that the manuscript currently lacks the requested quantitative details. In the revised version we will add a results table reporting success rates (with standard errors) for each encoding, the exact number of trials per condition (20 trials), and statistical comparisons (paired t-tests with p-values) against baselines. We will also expand the implementation details subsection to describe baseline adaptations, sensor calibration, and trial protocol on the real robot. revision: yes

  2. Referee: [§4.3] §4.3 (Test variations): The evaluation uses only discrete frame shifts in a fixed test environment; no experiments address continuous drifts, compounding errors, or multi-axis movements, so the claim that the encoding generalizes to broader real-world frame changes lacks direct evidence.

    Authors: The study deliberately used controlled discrete frame shifts to isolate the effect of reference-frame mismatch, which is the core practical problem addressed by the paper. We will revise the text to clarify that the reported robustness applies to the tested discrete shifts and will add an explicit limitations paragraph acknowledging that continuous drifts, compounding errors, and multi-axis variations remain untested. We will also suggest these as directions for future work rather than claiming broader generalization. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical comparison with no derivations or self-referential fits

full rationale

The paper conducts a direct experimental evaluation of proprioceptive state encodings on real robots, comparing task performance and robustness across in- and out-of-distribution conditions. The central result—that an episode-wise relative frame yields the best trade-off—is obtained by measuring outcomes on held-out test configurations rather than by any equation, parameter fit, or uniqueness theorem that reduces to the inputs by construction. No self-citations are invoked to justify load-bearing premises, no ansatzes are smuggled, and no known empirical patterns are merely renamed. The derivation chain is therefore empty; the claim rests on observable experimental differences and is self-contained against the reported benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Work is purely empirical; no free parameters, axioms, or invented entities are invoked in the abstract.

pith-pipeline@v0.9.0 · 5488 in / 882 out tokens · 35420 ms · 2026-05-14T19:03:32.513350+00:00 · methodology

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Reference graph

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