F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation

• URL: http://arxiv.org/abs/2604.02408v1
• Date: Thu, 02 Apr 2026 17:57:15 GMT
• Title: F2F-AP: Flow-to-Future Asynchronous Policy for Real-time Dynamic Manipulation
• Abstract summary: Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation.<n>In this paper, we propose a novel framework that leverages predicted object flow to synthesize future observations.<n>Our approach significantly enhances responsiveness and success rates in complex dynamic manipulation tasks.
• Score: 62.06267255986041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asynchronous inference has emerged as a prevalent paradigm in robotic manipulation, achieving significant progress in ensuring trajectory smoothness and efficiency. However, a systemic challenge remains unresolved, as inherent latency causes generated actions to inevitably lag behind the real-time environment. This issue is particularly exacerbated in dynamic scenarios, where such temporal misalignment severely compromises the policy's ability to interpret and react to rapidly evolving surroundings. In this paper, we propose a novel framework that leverages predicted object flow to synthesize future observations, incorporating a flow-based contrastive learning objective to align the visual feature representations of predicted observations with ground-truth future states. Empowered by this anticipated visual context, our asynchronous policy gains the capacity for proactive planning and motion, enabling it to explicitly compensate for latency and robustly execute manipulation tasks involving actively moving objects. Experimental results demonstrate that our approach significantly enhances responsiveness and success rates in complex dynamic manipulation tasks.

Related papers

• FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking [51.97839311685636]
  FutureRTC is a plug-and-play adaptation framework that predicts execution-time observations and states for asynchronous VLA control without modifying the underlying policy.<n>We show that FutureRTC achieves robustness to inference delays, resulting in smoother trajectories, faster execution, and consistently higher task success rates.
  arXiv  Detail & Related papers  (2026-07-27T05:10:16Z)
• GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking [71.9852231453445]
  Event cameras enable low-latency and motion-robust perception, making them well-suited for feature tracking under fast and non-linear motion.<n>Existing event-based feature tracking methods rely on fixed rules based on hand-tuning for event accumulation.<n>We introduce reinforcement learning framework to adaptively control the accumulation process for online event-based feature tracking.
  arXiv  Detail & Related papers  (2026-07-17T07:18:07Z)
• 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)
• Learning When to Listen: Gated Affect Fusion for Human Motion Prediction [0.0]
  We present a systematic study investigating the utility and temporal limitations of affect-conditioned forecasting in-the-wild.<n>We introduce the Gated Affect Transformer (GAT) to dynamically regulate cross-modal information flow.<n>Our findings provide empirical evidence that facial affect should be regarded as a complementary behavioral cue.
  arXiv  Detail & Related papers  (2026-07-01T00:47:02Z)
• From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation [88.39072412680633]
  We propose MoLA, a control-oriented interface that transforms imagined future videos into executable representations.<n>We evaluate our approach on simulated benchmarks and real-world robot manipulation tasks.
  arXiv  Detail & Related papers  (2026-05-12T14:15:16Z)
• Reactive Motion Generation With Particle-Based Perception in Dynamic Environments [14.960949776319147]
  Reactive motion generation in dynamic and unstructured scenarios is typically subject to essentially static perception and system dynamics.<n>This article focuses on revealing tight connection between reactive planning and dynamic mapping for manipulators from a model-based perspective.<n>We propose an obstacle-aware MPPI-based planning formulation that jointly propagates robot-obstacle dynamics.
  arXiv  Detail & Related papers  (2026-02-18T13:48:54Z)
• CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation [4.726851899243877]
  LG-Flow Policy is a trajectory-level imitation learning framework that performs flow matching in a continuous latent action space.<n>By encoding action sequences into temporally regularized latent trajectories and learning an explicit latent-space flow, the proposed approach decouples global motion structure from low-level control noise.
  arXiv  Detail & Related papers  (2026-01-30T15:36:43Z)
• Real-Time Robot Execution with Masked Action Chunking [38.37108371991901]
  Real-time execution is essential for cyber-physical systems such as robots.<n>Asynchronous inference has recently emerged as a system-level paradigm for real-time robot manipulation.<n>We propose REMAC, which learns corrective adjustments on the pretrained policy through masked action chunking.
  arXiv  Detail & Related papers  (2026-01-27T23:48:32Z)
• 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)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark [49.58762201363483]
  We introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments. We propose a Dual-stream Motion-Scenario Decoupling framework that effectively separates scenario-oriented and motion-oriented features. We demonstrate significant performance improvements of the proposed textitDMSD framework on different difficulty-level tasks.
  arXiv  Detail & Related papers  (2023-05-17T14:14:31Z)
• Motion Deblurring with Real Events [50.441934496692376]
  We propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner. Real-world events are exploited to alleviate the performance degradation caused by data inconsistency.
  arXiv  Detail & Related papers  (2021-09-28T13:11:44Z)

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.