Action Chunking

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16 papers in the last four weeks, against 2 the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 53

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SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies
Jul 6, 2026Dijia Zhan, Xuemiao Xu, Jinyi Li +1Vision-Language-Action Models Action Chunking
South China University of Technology
VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
Jul 2, 2026Yi Pan, Miao Pan, Qi Lu +8Long-Horizon Robotic Manipulation Feedback Control
Zhejiang University · Alibaba DAMO Academy
Action ControlNet: A Lightweight Delay-Aware Adapter for Smooth Asynchronous Control in Vision-Language-Action Models
Jun 24, 2026Tiecheng Guo, Meng GuoRobotic Control Language-Conditioned Robot Manipulation
The authors are with the School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China.
PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models
Jun 21, 2026Xianghui Wang, Feng Chen, Wenbo Zhang +4Efficient VLA Models Vision-Language-Action Models
1Sichuan University · 2Adelaide University · 3Beijing Institute of Technology
Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection
Jun 18, 2026Trong-Bao Ho, Quang-Tan Nguyen, Thien-Loc Ha +7Robotic Control Robot Policy Learning
VinRobotics · VinUniversity · DFKI +2
DREAM-Chunk: Reactive Action Chunking with Latent World Model
Jun 17, 2026Wenxi Chen, Kaidi Zhang, Chi Lin +6Test-Time Scaling Robotic Manipulation
Purdue University · Stanford University
Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration
Jun 10, 2026Leo Xu, Letian Li, Alex Cuellar +1Long-Horizon Robotic Manipulation Human-Robot Collaboration
University of Wisconsin–Madison · Massachusetts Institute of Technology
Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
Jun 9, 2026Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal +5Robot Policy Learning Long-Horizon Robotic Manipulation
Department of Computer Science, University of Toronto, 40 St George St., Toronto, ON M5S 2E4, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada +7
Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
Jun 2, 2026Xiangdong Feng, Yuxuan Cheng, Chen Shi +5Robot Policy Learning Action Chunking
Beijing Institute of Technology · Shenzhen Loop Area Institute · The Chinese University of Hong Kong, Shenzhen +4
Threading Optimization for Vision-Language-Action Model Inference in Low-Cost Smart Agricultural Manipulation
May 31, 2026Keith Truongcao, Christopher Nhu, Zijian An +3Efficient VLA Model Inference Robotic Manipulation
Department of Electrical Engineering, Drexel University, Philadelphia, USA
PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
May 30, 2026Junnan Nie, Jiayi Li, Chenghao Liu +5Robotic Manipulation Action Chunking
Peking University · Peking University. · JD Explore Academy. +1
Action-Prior Denoising for Smooth Real-Time Chunking
May 25, 2026Dongyang Liu, Zhaowen Zheng, Yu Sun +3Robot Policy Learning Action Chunking
ROKAE (Shandong) Robot Group Co., Ltd. · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Learning High-Frequency Continuous Action Chunks in Latent Space
May 24, 2026Kunyun Wang, Yuhang Zheng, Yupeng Zheng +2Robotic Control Latent Action Learning
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · TARS Robotics · National University of Singapore, Singapore +2
Smoother Action Chunking Flow Policy via Prior-Corrected Orthogonal Trust-Region Guidance
May 23, 2026Kai Fang, Hailong Pei, Xuemin ChiRobot Policy Learning Action Chunking
South China University of Technology · Guangzhou, China · Zhejiang University +1
Implicit Action Chunking for Smooth Continuous Control
May 19, 2026Bosun Liang, Shuo Pei, Zirui Chen +5RL Control Continuous Control
Department of Data and Systems Engineering, The University of Hong Kong, Hong Kong SAR, China · Beijing Institute of Technology, Zhuhai, China · College of Computer Science, Sichuan University, Chengdu, China
FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy
May 15, 2026Qian He, Zhenshuo Yang, Wenqi Liang +3Flow Matching Long-Horizon Robotic Manipulation
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences · University of the Chinese Academy of Sciences · University of Trento
Dynamic Execution Commitment of Vision-Language-Action Models
May 12, 2026Feng Chen, Xianghui Wang, Yuxuan Chen +4VLM Robustness Efficient VLM Inference
University of Adelaide · Sichuan University · Shanghai Jiao Tong University +3
Adaptive Action Chunking via Multi-Chunk Q Value Estimation
May 11, 2026Yongjae Shin, Jongseong Chae, Seongmin Kim +2Q-Learning Action Chunking
KAIST
PACE: Policy-Native Adaptive Decision Timing for Long-Horizon Reasoning
May 11, 2026Chen Li, Zhantao Yang, Fangyi Chen +3Large Vision-Language Models Long-Horizon Planning
Carnegie Mellon University
ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network
May 10, 2026Qian Chen, Junqiao Zhao, Hongtu Zhou +4Reinforcement Learning Transformer-Based RL
Tongji University
Adaptive Q-Chunking for Offline-to-Online Reinforcement Learning
May 7, 2026Nandiraju Gireesh, Yuanliang Ju, He WangQ-Learning Offline RL
Peking University · Galbot · University of Toronto
LiveVLN: Breaking the Stop-and-Go Loop in Vision-Language Navigation
Apr 21, 2026Xiangchen Wang, Weiye Zhu, Teng Wang +5Vision-Language Navigation Action Chunking
Southern University of Science and Technology · Harbin Institute of Technology, Shenzhen · Spatialtemporal AI
MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Task-Conditioned Mixture-of-Experts Transformers
Mar 16, 2026Kangjun Guo, Haichao Liu, Yanji Sun +3Bimanual Robotic Manipulation Visuomotor Policy Learning
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China

Jul 6, 2026, cs.ROJ/K move · Enter open · S save

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

Dijia Zhan, Xuemiao Xu, Jinyi Li, Jie Tang

South China University of Technology

Vision-Language-Action Models Action Chunking

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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.

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