Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation

• URL: http://arxiv.org/abs/2604.05544v1
• Date: Tue, 07 Apr 2026 07:41:11 GMT
• Title: Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
• Abstract summary: We introduce the Referring-Aware Visuomotor Policy (ReV)<n>ReV incorporates sparse referring points provided by a human or a high-level reasoning planner.<n>It is trained only by applying targeted perturbations to expert demonstrations.
• Score: 91.20850436220267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses a fundamental problem of visuomotor policy learning for robotic manipulation: how to enhance robustness in out-of-distribution execution errors or dynamically re-routing trajectories, where the model relies solely on the original expert demonstrations for training. We introduce the Referring-Aware Visuomotor Policy (ReV), a closed-loop framework that can adapt to unforeseen circumstances by instantly incorporating sparse referring points provided by a human or a high-level reasoning planner. Specifically, ReV leverages the coupled diffusion heads to preserve standard task execution patterns while seamlessly integrating sparse referring via a trajectory-steering strategy. Upon receiving a specific referring point, the global diffusion head firstly generates a sequence of globally consistent yet temporally sparse action anchors, while identifies the precise temporal position for the referring point within this sequence. Subsequently, the local diffusion head adaptively interpolates adjacent anchors based on the current temporal position for specific tasks. This closed-loop process repeats at every execution step, enabling real-time trajectory replanning in response to dynamic changes in the scene. In practice, rather than relying on elaborate annotations, ReV is trained only by applying targeted perturbations to expert demonstrations. Without any additional data or fine-tuning scheme, ReV achieve higher success rates across challenging simulated and real-world tasks.

Related papers

• SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation [65.81476183180527]
  We introduce SegDiff, a closed-loop visuomotor policy that integrates the strengths of both paradigms.<n>SegDiff decomposes demonstrations into motion segments between keyposes and learns to predict the continuous trajectory from the current state to the next keypose.<n>SegDiff demonstrates significant performance gains over existing approaches across various simulated and real-world scenarios.
  arXiv  Detail & Related papers  (2026-07-13T02:48:03Z)
• Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization [31.035684025590417]
  Sparse rewards pose a central challenge in reinforcement learning, since agents receive no informative signal until they reach their goal.<n>We present a method to pre-train transferable exploration policies that rapidly adapt to sparse rewards at downstream task time.
  arXiv  Detail & Related papers  (2026-06-19T09:47:38Z)
• Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models [54.16797570104461]
  Diffusion-based language models (dLLMs) have emerged as a promising alternative to autoregressive language models.<n>We investigate non-autoregressive decoding in dLLMs by systematically analyzing its inference dynamics along the temporal axis.
  arXiv  Detail & Related papers  (2026-04-12T10:26:41Z)
• FASTER: Rethinking Real-Time Flow VLAs [82.58822112377923]
  Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world.<n>We show that reaction time follows a uniform distribution determined jointly by the Time to First Action (TTFA) and the execution horizon.<n>We propose Fast Action Sampling for ImmediaTE Reaction (FASTER) to overcome this issue.
  arXiv  Detail & Related papers  (2026-03-19T17:51:37Z)
• GraspLDP: Towards Generalizable Grasping Policy via Latent Diffusion [44.168491831527355]
  This paper focuses on enhancing the grasping precision and generalization of manipulation policies learned via imitation learning.<n>Existing imitation learning techniques for grasping often suffer from imprecise grasp executions, limited spatial generalization, and poor object generalization.
  arXiv  Detail & Related papers  (2026-02-26T10:56:01Z)
• Generalizing from References using a Multi-Task Reference and Goal-Driven RL Framework [12.131501436717969]
  We introduce a unified multi-task Reinforcement Learning framework for learning humanoid behaviors from human motion.<n>A single goal-conditioned policy is trained jointly on two tasks that share the same observation and action spaces.<n>By co-optimizing these objectives within a shared formulation, the policy acquires structured, human-like motor skills from dense reference supervision.
  arXiv  Detail & Related papers  (2026-02-23T21:25:06Z)
• Trust Region Continual Learning as an Implicit Meta-Learner [3.705371747297478]
  We study a hybrid perspective: emphtrust region continual learning that combines generative replay with a Fisher-metric trust region constraint.<n>We show that, under local approximations, the resulting update admits a MAML-style interpretation with a single implicit inner step.<n>This yields an emergent meta-learning property in continual learning.
  arXiv  Detail & Related papers  (2026-02-02T18:19:16Z)
• ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning [52.86018040861575]
  We propose a unified end-to-end visual-force diffusion policy that integrates visual planning and reactive force control within a single network.<n>We introduce Structural Slow-Fast Learning, a mechanism utilizing causal attention to simultaneously process asynchronous visual and force tokens.<n>Experiments on contact-rich tasks demonstrate that ImplicitRDP significantly outperforms both vision-only and hierarchical baselines.
  arXiv  Detail & Related papers  (2025-12-11T18:59:46Z)
• CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance [15.311155448797386]
  contexts-aware diffusion policy via Proximal mode Expansion (CAPE)<n>CAPE expands trajectory distribution modes with context-aware prior and guidance at inference.<n>We evaluate CAPE on diverse manipulation tasks in cluttered unseen simulated and real-world settings.
  arXiv  Detail & Related papers  (2025-11-27T21:53:09Z)
• TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning [79.59753528758361]
  We present TrajBooster, a cross-embodiment framework that leverages abundant wheeled-humanoid data to boost bipedal VLA.<n>Our key idea is to use end-effector trajectories as a morphology-agnostic interface.<n>Results show that TrajBooster allows existing wheeled-humanoid data to efficiently strengthen bipedal humanoid VLA performance.
  arXiv  Detail & Related papers  (2025-09-15T12:25:39Z)
• Generative Regression Based Watch Time Prediction for Short-Video Recommendation [36.95095097454143]
  Watch time prediction has emerged as a pivotal task in short video recommendation systems.<n>Recent studies have attempted to address these issues by converting the continuous watch time estimation into an ordinal regression task.<n>We propose a novel Generative Regression (GR) framework that reformulates WTP as a sequence generation task.
  arXiv  Detail & Related papers  (2024-12-28T16:48:55Z)
• Foundation Policies with Hilbert Representations [54.44869979017766]
  We propose an unsupervised framework to pre-train generalist policies from unlabeled offline data. Our key insight is to learn a structured representation that preserves the temporal structure of the underlying environment. Our experiments show that our unsupervised policies can solve goal-conditioned and general RL tasks in a zero-shot fashion.
  arXiv  Detail & Related papers  (2024-02-23T19:09:10Z)
• Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows [58.762959061522736]
  offline reinforcement learning aims to train a policy on a pre-recorded and fixed dataset without any additional environment interactions. We build upon recent works on learning policies in latent action spaces and use a special form of Normalizing Flows for constructing a generative model. We evaluate our method on various locomotion and navigation tasks, demonstrating that our approach outperforms recently proposed algorithms.
  arXiv  Detail & Related papers  (2022-11-20T21:57:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.