SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies

• URL: http://arxiv.org/abs/2607.04609v1
• Date: Mon, 06 Jul 2026 02:32:10 GMT
• Title: SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies
• Abstract summary: Vision-Language-Action (VLA) policies that execute fixed-length action chunks can exhibit multimodal bifurcation.<n>We propose SEAM (Smooth Execution of Action-Chunked Motion), a training-free inference-time method for flow matching VLAs.<n>On LIBERO-10 with pi_0.5, SEAM reduces boundary jerk by 28%, reduces chunk transition discontinuity by 27%, preserves baseline-level task success, and keeps denoising-loop cost near the unguided baseline.
• Score: 26.372187124257362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) policies that execute fixed-length action chunks can exhibit multimodal bifurcation: a cross-chunk inconsistency in which adjacent chunks generated from independent Gaussian latents can converge to incompatible trajectory modes, producing abrupt discontinuities at chunk boundaries. Existing remedies either require backpropagation through the policy at each denoising step, rely on rejection sampling, or require retraining, each trading computational cost or task reliability for smoother transitions. We propose SEAM (Smooth Execution of Action-Chunked Motion), a training-free inference-time method for flow matching VLAs. SEAM exploits a simple synchronous-execution insight: after the robot consumes the executed prefix, the previous chunk's unexecuted tail is already available as an analytic consistency reference. Its core mechanism, Velocity-guided Loss Steering (VLS), derives a time-dependent target from this tail and applies a closed-form correction after each Euler step without backpropagating through the policy network. On LIBERO-10 with pi_0.5, SEAM reduces boundary jerk by 28%, reduces chunk transition discontinuity by 27%, preserves baseline-level task success, and keeps denoising-loop cost near the unguided baseline.

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)
• ChunkFlow: Towards Continuity-Consistent Chunked Policy Learning [5.727608625370017]
  ChunkFlow is a seam-aware training-and-execution framework for chunked policies.<n>It partitions each chunk into frozen, editable, and future zones, applies deterministic overlap blending at execution, and trains raw predictions with seam and first- and second-order continuity losses.<n> Experiments on CALVIN, LIBERO, and real robots show an improved success-stability trade-off with low-latency inference.
  arXiv  Detail & Related papers  (2026-07-14T17:43:25Z)
• VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon [40.99993238069073]
  VLA-Corrector is a lightweight corrective inference framework for action-chunked Vision-Language-Action policies.<n>It can be integrated into different VLA models without further retraining the VLA backbone.<n>It mitigates the trade-off imposed by static horizons between execution and policy-call frequency.
  arXiv  Detail & Related papers  (2026-07-02T07:18:53Z)
• Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies [18.476299874941592]
  Action chunking has become a common inference strategy for flow-based robot policies.<n>We propose DVAC, a test-time method that adaptively determines how many actions to execute from each predicted chunk.<n>Experiments on LIBERO, RoboTwin, CALVIN, and real-world manipulation show that DVAC improves task success while reducing replanning frequency.
  arXiv  Detail & Related papers  (2026-06-02T16:26:32Z)
• DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors [57.944744187489185]
  We propose DiscreteRTC, which replaces external corrections with native unmasking.<n>DiscreteRTC is simpler to implement with 0 lines of code for async inpainting, faster at inference with only 0.7x computation compared with generating actions from scratch, and better at execution with 50% higher success rate in real-world dynamic pick task compared with flow-based RTC.
  arXiv  Detail & Related papers  (2026-04-27T23:04:03Z)
• A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model [112.9420001646428]
  Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often constrained by cost.<n>We present A1, a fully open-source and transparent VLA framework designed for low-cost, high- throughput inference.<n>A1 achieves state-of-the-art success rates while significantly reducing inference cost.
  arXiv  Detail & Related papers  (2026-04-07T10:18:40Z)
• AnchorVLA: Anchored Diffusion for Efficient End-to-End Mobile Manipulation [36.801575461006664]
  A central challenge in mobile manipulation is preserving plausible action models while remaining reactive during execution.<n>We present AnchorVLA, a diffusion-based VLA policy for mobile manipulation built on the core insight that when sampling begins near a plausible solution manifold, extensive denoising is unnecessary.
  arXiv  Detail & Related papers  (2026-04-02T03:29:04Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• FAST-DIPS: Adjoint-Free Analytic Steps and Hard-Constrained Likelihood Correction for Diffusion-Prior Inverse Problems [2.9506605740700107]
  Training-free diffusion priors often rely on repeated derivatives or inner optimization/MCMC loops with conservative step sizes.<n>We propose a training-free solver that replaces these inner loops with a hard measurement-space feasibility constraint.<n>Experiments achieve competitive PSNR/SSIM/LPIPS with up to 19.5$times$ speedup, without hand-coded adjoints or inner MCMC.
  arXiv  Detail & Related papers  (2026-03-02T08:17:26Z)
• Learning Native Continuation for Action Chunking Flow Policies [40.56048312294812]
  Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries.<n>We propose Legato, a training-time continuation method for action-chunked flow-based VLA policies.<n>Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time.
  arXiv  Detail & Related papers  (2026-02-13T14:56:06Z)
• Unbiased Gradient Estimation for Event Binning via Functional Backpropagation [64.88399635309918]
  We propose a novel framework for unbiased gradient estimation of arbitrary binning functions by synthesizing weak derivatives during backpropagation.<n>We achieve 9.4% lower EPE in self-supervised optical flow, and 5.1% lower RMS error in SLAM, demonstrating broad benefits for event-based visual perception.
  arXiv  Detail & Related papers  (2026-02-13T04:05:03Z)
• Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach [78.4812458793128]
  We propose textbfTACO, a test-time-scaling framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks.<n>Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits.
  arXiv  Detail & Related papers  (2025-12-02T14:42:54Z)

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.