PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking

• URL: http://arxiv.org/abs/2606.00537v1
• Date: Sat, 30 May 2026 05:11:06 GMT
• Title: PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
• Abstract summary: PACE is a training-free test-time execution method that selects the execution horizon online from the predicted chunk itself.<n>Because PACE uses only the predicted action chunk, it is plug-and-play and requires no retraining or access to policy internals.<n>We validate PACE through large-scale evaluations in both simulation and real-robot settings.
• Score: 37.9894332388921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent vision-language-action and diffusion-based robot policies often use action chunking, where each policy query predicts a sequence of future actions and the robot executes an open-loop prefix before re-querying. While this interface improves local motion continuity, deployment still requires choosing the execution horizon: how much of each predicted chunk should be executed before acquiring a new observation. However, our experiments show that success is strongly task-dependent and non-monotonic with respect to the execution horizon, making a single constant horizon an unreliable deployment rule. We propose PACE (Phase-Aware Chunk Execution), a training-free test-time execution method that selects the execution horizon online from the predicted chunk itself. PACE exploits the phase-dependent kinematic structure of manipulation trajectories by identifying low-speed transition points in the predicted speed profile and using them as candidate replanning boundaries. Because PACE uses only the predicted action chunk, it is plug-and-play and requires no retraining or access to policy internals. We validate PACE through large-scale evaluations in both simulation and real-robot settings. On 50 RoboTwin2.0 tasks, PACE raises the average success rate from 57.8% to 64.2%. In real-robot experiments on bimanual ALOHA and single-arm Franka platforms, PACE improves the average task score from 60.7 to 77.7 and the average success rate from 50.7% to 70.4%. Ablations and rollout-level analyses show that PACE adapts execution horizons across manipulation phases, shortening near transitions while preserving longer execution during coherent motion.

Related papers

• Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents [50.85250898062218]
  Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol.<n>A natural alternative is to let the agent emit variable-length action chunks.<n>But naively training such policies with standard reinforcement learning fails.
  arXiv  Detail & Related papers  (2026-09-02T03:17:11Z)
• EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents [56.25120535601186]
  Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions.<n>We propose EmbodiedSkills, a framework that treats each skill decision as an execution proposal.<n>A shared executable-skill interface connects high-level skill selection, bounded low-level VLA execution, and post-action verification.
  arXiv  Detail & Related papers  (2026-09-01T14:14:47Z)
• TemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation [14.84720789511593]
  We present TemporalFlow-VLA, which learns compact execution history through physically grounded temporal supervision.<n>We show that TemporalFlow-VLA achieves 97.63 +/- 0.26% average success on LIBERO, including 96.60 +/- 0.87% on LIBERO Long, and 85.5%/84.2% Clean/Randomized success across 12 RoboTwin tasks.
  arXiv  Detail & Related papers  (2026-08-27T09:00:56Z)
• LM-X: Explainable Action Modeling with Progress, Event, and Uncertainty Prediction for Generalist Robot Manipulation [28.431160101245837]
  Generalist vision--language--action (VLA) policies learn long-horizon behavior mainly through short-horizon action prediction.<n>We introduce LM-X, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence.
  arXiv  Detail & Related papers  (2026-08-26T13:05:29Z)
• DreamTrajectory: Trajectory-Guided Action Generation with World Model Alignment for Mobile Manipulation [33.046851310859495]
  We present DreamTrajectory, a trajectory-guided framework for language-conditioned mobile manipulation.<n>On MS-HAB, trajectory guidance raises average success from 32.3% to 47.5% and test-time refinement further to 54.8%.<n>On three real-world mobile manipulation tasks, the corresponding average success rates are 63.3%, 81.7%, and 90.0%.
  arXiv  Detail & Related papers  (2026-08-02T17:02:48Z)
• 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)
• Dynamic Execution Horizon Prediction for Chunk-based Robot Policies [29.482987292744568]
  Action chunking has become a standard design in modern robot policies, where the policy predicts a sequence of actions and executes a fixed number of them instead of acting one step at a time.<n>In practice, the execution horizon is typically chosen through empirical tuning and is highly task-dependent.<n>We propose Dynamic Execution Horizon Prediction (DEHP), an effective method that trains a lightweight execution-horizon prediction branch using online reinforcement learning.
  arXiv  Detail & Related papers  (2026-06-09T19:58:31Z)
• CADENCE: Predicting Realized MAPF Execution Time Beyond Sum of Costs [0.8602553195689513]
  Multi-Agent Path Finding (MAPF) algorithms are increasingly used to plan motion for robot teams in industrial warehouses and robotic shared workspaces.<n>But standard MAPF algorithm evaluation metrics, such as Sum of Costs (SoC), makespan, and planner runtime, can obscure how planner choices translate into realistic execution performance.<n>We present CADENCE, a hardware study of this evaluation gap on a fixed 7 by 7 workcell with seven differential drive robots.
  arXiv  Detail & Related papers  (2026-06-03T11:28:09Z)
• When to Trust Imagination: Adaptive Action Execution for World Action Models [42.51856318901667]
  World Action Models (WAMs) have recently emerged as a promising paradigm for robotic manipulation by jointly predicting future visual observations and future actions.<n>Current WAMs execute a fixed number of predicted actions after each model inference, leaving the robot blind to whether the imagined future remains consistent with the actual physical rollout.<n>We formulate adaptive WAM execution as a future-reality verification problem: the robot should execute longer when the WAM-predicted future remains reliable, and replan earlier when reality deviates from imagination.
  arXiv  Detail & Related papers  (2026-05-07T13:18:28Z)
• STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation [30.24537271257149]
  STARRY is a world-model-enhanced action-generation policy that aligns spatial-temporal prediction and action generation.<n>On RoboTwin 2.0, STARRY achieves 93.82% / 93.30% average success under Clean and Randomized settings.<n>These results demonstrate the effectiveness of action-centric spatial-temporal world modeling for spatially and temporally demanding robotic manipulation.
  arXiv  Detail & Related papers  (2026-04-29T16:13:39Z)
• ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation [57.07494675832939]
  Most existing vision-language-action (VLA) models for robotic manipulation lack progress awareness.<n>In this work, we investigate the estimation and integration of task progress, proposing a novel model named textbf vla<n>Experiments on the CALVIN and LIBERO benchmarks, alongside real-world robot deployment, consistently demonstrate substantial improvements in success rates.
  arXiv  Detail & Related papers  (2026-03-29T12:38:11Z)
• 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)
• Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation [95.89924101984566]
  We introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM)<n>GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories.<n>LCM injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory.
  arXiv  Detail & Related papers  (2026-02-22T15:39:34Z)
• 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)
• PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation [27.791908160098625]
  PALM builds policy learning around interaction-centric affordance reasoning and subtask progress cues.<n>Palm consistently outperforms baselines in simulation and real-world experiments.
  arXiv  Detail & Related papers  (2026-01-11T21:00:58Z)
• FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning [74.25049012472502]
  FLaRe is a large-scale Reinforcement Learning framework that integrates robust pre-trained representations, large-scale training, and gradient stabilization techniques. Our method aligns pre-trained policies towards task completion, achieving state-of-the-art (SoTA) performance on previously demonstrated and on entirely novel tasks and embodiments.
  arXiv  Detail & Related papers  (2024-09-25T03:15:17Z)

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.