OpenNovelty.org

GAP: Gradient Adjustment with Phase-guidance for Robust Vision-Proprioception Policies in Robotic Manipulation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Robotic ManipulationVision-Proprioception Policy
Abstract:

Proprioceptive information is critical for precise servo control by providing real-time robotic states. Its collaboration with vision is highly expected to enhance performances of the manipulation policy in complex tasks. However, recent studies have reported inconsistent observations on the generalization of vision-proprioception policies. In this work, we investigate this by conducting temporally controlled experiments. We found that during task sub-phases that robot's motion transitions, which require target localization, the vision modality of the vision-proprioception policy plays a limited role. Further analysis reveals that the policy naturally gravitates toward concise proprioceptive signals that offer faster loss reduction when training, thereby dominating the optimization and suppressing the learning of the visual modality during motion-transition phases. To alleviate this, we propose the Gradient Adjustment with Phase-guidance (GAP) algorithm that adaptively modulates the optimization of proprioception, enabling dynamic collaboration within the vision-proprioception policy. Specifically, we leverage proprioception to capture robotic states and estimate the probability of each timestep in the trajectory belonging to motion-transition phases. During policy learning, we apply fine-grained adjustment that reduces the magnitude of proprioception's gradient based on estimated probabilities, leading to robust and generalizable vision-proprioception policies. The comprehensive experiments demonstrate GAP is applicable in both simulated and real-world environments, across one-arm and dual-arm setups, and compatible with both conventional and Vision-Language-Action models. We believe this work can offer valuable insights into the development of vision-proprioception policies in robotic manipulation.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a Gradient Adjustment with Phase-guidance (GAP) algorithm to address vision modality suppression in vision-proprioception policies during motion-transition phases. It resides in the Vision-Proprioception Fusion Frameworks leaf, which contains five papers including the original work. This leaf sits within the broader Multimodal Sensory Integration Architectures branch, indicating a moderately populated research direction focused specifically on integrating visual and proprioceptive signals without tactile sensing. The taxonomy shows this is an active but not overcrowded area, with neighboring leaves exploring visuotactile integration and cross-modal representation learning.

The taxonomy reveals several related directions that contextualize this work. The sibling leaf Visuotactile and Force-Aware Integration contains twelve papers addressing contact-rich tasks with additional sensing modalities, while Cross-Modal Representation Learning (four papers) explores self-supervised approaches for shared representations. The Policy Learning Paradigms branch encompasses reinforcement learning, imitation learning, and vision-language-action models, suggesting that fusion architectures like GAP must interface with diverse training strategies. The paper's focus on phase-based modulation distinguishes it from end-to-end fusion methods that treat all task stages uniformly.

Among sixteen candidates examined across three contributions, none were found to clearly refute the proposed work. The core contribution—identifying vision suppression during motion transitions—examined ten candidates with zero refutable matches, suggesting this specific temporal analysis perspective may be relatively unexplored in the limited search scope. The GAP algorithm itself examined five candidates without refutation, while the motion-transition phase estimation framework examined only one candidate. These statistics indicate that within the top-K semantic matches retrieved, no prior work directly anticipates the phase-guided gradient adjustment mechanism or the empirical observation of modality imbalance during specific task sub-phases.

Based on the limited literature search of sixteen candidates, the work appears to introduce a novel perspective on temporal dynamics in multimodal policy learning. The analysis does not claim exhaustive coverage of all related work in vision-proprioception fusion, and the relatively small candidate pool means potentially relevant papers outside the top semantic matches may exist. The taxonomy structure suggests the paper occupies a moderately explored niche, with sufficient prior work to establish context but enough sparsity to accommodate new architectural insights around phase-aware optimization.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
16
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: multimodal policy learning for robotic manipulation with vision and proprioception. The field has evolved into a rich landscape organized around several complementary dimensions. Multimodal Sensory Integration Architectures explore how to fuse visual observations with proprioceptive signals—and increasingly tactile feedback—into coherent representations that guide action. Policy Learning Paradigms address the algorithmic side, spanning imitation learning, reinforcement learning, and hybrid approaches that leverage large-scale datasets or foundation models. Task-Specific Manipulation Strategies focus on particular problem settings such as dexterous grasping, insertion, or deformable object handling, while Perception and Reasoning for Manipulation examines higher-level scene understanding and affordance prediction. Data Collection and Benchmarking Infrastructure provides the datasets and evaluation protocols that enable systematic progress, and Visual Representation and Pretraining for Manipulation investigates how pretrained vision models can transfer to robotic control. Representative works like Droid Dataset[3] and Scaling Proprioceptive Visual[9] illustrate efforts to scale data and integrate multiple modalities, while Unified Manipulation Survey[8] offers a broader synthesis of these directions. Within this landscape, a particularly active line of work centers on Vision-Proprioception Fusion Frameworks, where researchers design architectures that tightly couple visual and proprioceptive streams to improve sample efficiency and generalization. GAP Phase Guidance[0] sits squarely in this cluster, emphasizing structured phase-based reasoning that leverages both modalities to guide manipulation policies through complex contact-rich tasks. Nearby efforts such as Fusion Perception Action[2] and Proprioceptive States Visuomotor[36] similarly investigate how to balance or interleave sensory channels, though they may differ in whether they prioritize end-to-end learning versus modular fusion strategies. Another contrasting theme emerges in works like Touch in Wild[1] and PolyTouch[6], which extend the sensory palette to include tactile signals, raising questions about when and how additional modalities justify their added complexity. Overall, GAP Phase Guidance[0] contributes to an ongoing conversation about designing interpretable, multimodal architectures that can scale to diverse manipulation scenarios while maintaining robustness in the face of partial observability and contact dynamics.

Claimed Contributions

Identification of vision modality suppression during motion-transition phases

The authors identify through temporally controlled experiments that vision-proprioception policies fail to effectively utilize visual information during motion-transition phases. They reveal that the policy gravitates toward concise proprioceptive signals during training, which dominates optimization and suppresses learning of the visual modality.

10 retrieved papers
Gradient Adjustment with Phase-guidance (GAP) algorithm

The authors propose GAP, an algorithm that uses proprioception to estimate the probability of each timestep belonging to motion-transition phases, then applies fine-grained gradient adjustment to reduce the magnitude of proprioception's gradient based on these probabilities. This enables robust and generalizable vision-proprioception policies.

5 retrieved papers
Motion-transition phase estimation framework

The authors develop a framework that defines robot motion representation using proprioceptive signals, segments trajectories into motion-consistent phases using Change Point Detection, and employs a temporal network (LSTM) to model transition processes and estimate motion-transition phase probabilities.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Identification of vision modality suppression during motion-transition phases

The authors identify through temporally controlled experiments that vision-proprioception policies fail to effectively utilize visual information during motion-transition phases. They reveal that the policy gravitates toward concise proprioceptive signals during training, which dominates optimization and suppresses learning of the visual modality.

Contribution

Gradient Adjustment with Phase-guidance (GAP) algorithm

The authors propose GAP, an algorithm that uses proprioception to estimate the probability of each timestep belonging to motion-transition phases, then applies fine-grained gradient adjustment to reduce the magnitude of proprioception's gradient based on these probabilities. This enables robust and generalizable vision-proprioception policies.

Contribution

Motion-transition phase estimation framework

The authors develop a framework that defines robot motion representation using proprioceptive signals, segments trajectories into motion-consistent phases using Change Point Detection, and employs a temporal network (LSTM) to model transition processes and estimate motion-transition phase probabilities.

Table of Contents