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Source note4 min read

UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies

Source

arXiv

arxiv.org/abs/2607.12892v1

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Source

Plain-Language Brief

Researchers introduce an offline method called Unsupervised Robotic Value Correction (UR-VC) to correct time-derived progress labels used by modern robot learning systems. The method is based on a regularity in demonstration data, where similar states often recur at different timestamps across different episodes. By aggregating the time-derived labels of similar states, UR-VC obtains a corrected progress estimate that improves upon the original time-derived label, especially for long-horizon deformable-object manipulation tasks with visible intermediate progress.

What the Document Says

  • Modern robot learning systems rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion. However, these dense labels are rarely available at scale.
  • A common substitute for the lack of dense labels is normalized time within a demonstration, where later frames are treated as higher progress. This time-derived label can be noisy in contact-rich manipulation due to slips, failed grasps, or partial undoing.
  • UR-VC is an offline, training-free method for correcting time-derived progress labels using a regularity in demonstration data: similar states often recur at different timestamps across different episodes.
  • UR-VC retrieves similar states from other episodes and aggregates their time-derived labels to obtain a corrected progress estimate.
  • UR-VC requires no manual progress labels, reward annotations, or additional value model.
  • The researchers evaluate UR-VC on real bimanual cloth flatten-and-fold data, a long-horizon deformable-object manipulation task with visible intermediate progress.
  • The corrected labels capture local regressions and non-uniform progress that normalized time cannot represent, while preserving the overall task trend.
  • The researchers further use the corrected signal to construct advantage labels for VLA training.

Key Concepts Mentioned

  • modern robot learning systems
  • dense progress or value signals
  • normalized time within a demonstration
  • contact-rich manipulation
  • slips
  • failed grasps
  • partial undoing
  • Unsupervised Robotic Value Correction (UR-VC)
  • offline method
  • training-free
  • similar states
  • aggregating time-derived labels
  • bimanual cloth flatten-and-fold data
  • long-horizon deformable-object manipulation task
  • visible intermediate progress
  • corrected labels
  • local regressions
  • non-uniform progress
  • advantage labels
  • VLA training

Claims Made by the Document

  • UR-VC is an effective method for correcting time-derived progress labels in modern robot learning systems.

Evidence Used

  • The researchers demonstrate the effectiveness of UR-VC on real bimanual cloth flatten-and-fold data, a long-horizon deformable-object manipulation task with visible intermediate progress.

Methods / Approach

  • UR-VC exploits a simple regularity in demonstration data to correct time-derived progress labels by retrieving similar states from other episodes and aggregating their time-derived labels

Limitations Stated by the Authors

  • UNKNOWN
  • The paper does not discuss potential limitations of UR-VC beyond the need for sufficient data and computational resources.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The effectiveness of UR-VC may be limited in tasks where similar states do not recur frequently or at different timestamps. Also, the method's performance might degrade when the task involves significant variations that are not captured by the retrieved similar states.

Open Questions

  • What is the computational complexity of UR-VC and how scalable is it to larger datasets?
  • How does UR-VC perform in tasks with infrequent recurrence of similar states or significant variations not captured by retrieved similar states?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The authors provide no information about the computational complexity or scalability of UR-VC.

Raw Abstract / Excerpt

Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the quality of these signals critical. Since such dense labels are rarely available at scale, normalized time within a demonstration is often used as a scalable substitute: later frames are treated as higher progress. However, this time-derived label is only a noisy proxy for physical task progress. In contact-rich manipulation, a robot may make progress and then lose it through slips, failed grasps, or partial undoing, while the time-derived label continues to increase monotonically. We introduce Unsupervised Robotic Value Correction (UR-VC), an offline, training-free method for correcting time-derived progress labels. UR-VC exploits a simple regularity in demonstration data: similar states often recur across different episodes, but at different timestamps. Instead of trusting the timestamp from a single trajectory, UR-VC retrieves similar states from other episodes and aggregates their time-derived labels to obtain a corrected progress estimate. UR-VC requires no manual progress labels, reward annotations, or additional value model. We evaluate UR-VC on real bimanual cloth flatten-and-fold data, a long-horizon deformable-object manipulation task with visible intermediate progress. The corrected labels capture local regressions and non-uniform progress that normalized time cannot represent, while preserving the overall task trend. We further use the corrected signal to construct advantage labels for VLA training, following recent advantage-conditioned policy learning. UR-VC shows a positive trend in real-robot task success under matched data, model, and training settings.