Visuomotor Policy Learning

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43 papers in the last four weeks, up 378% on the four weeks before. 0.4% of all new papers.

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LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
May 11, 2026Boyang Shen, Kaixiang Yang, Hao Wang +4Efficient VLM Inference Efficient VLA Model Inference
1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
May 9, 2026Antong Zhang, Han Qi, Heng YangSynthetic-to-Real Domain Adaptation Robot Policy Learning
Department of Computer Science, Brown University · School of Engineering and Applied Sciences, Harvard University
Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
May 8, 2026Riad Ahmed, Sujosh Nag, Moniruzzaman Akash +2Flow Matching Robot Policy Learning
University of New Hampshire, Durham, NH, USA
BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly
May 8, 2026Jichuan Yu, Bowei Li, Zhenran Tang +4Robotic Construction Long-Horizon Robotic Manipulation
Tsinghua University · Carnegie Mellon University
Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control
May 2, 2026Jinhao Zhang, Zhexuan Zhou, Huizhe Li +5Visuomotor Policy Learning Diffusion Policy
Harbin Institute of Technology, Shenzhen · Shanghai Jiao Tong University
To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations
May 2, 2026Riad Ahmed, Moniruzzaman Akash, Momotaz BegumVisuomotor Policy Learning Imitation Learning
Department of Computer Science University of New Hampshire
MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation
May 1, 2026Xianbo Cai, Hideyuki Ichiwara, Masaki Yoshikawa +1Bimanual Robotic Manipulation Visuomotor Policy Learning
Department of Intermedia Art and Science, Waseda University, Tokyo, Japan · National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
Stereo Multistage Spatial Attention for Real-Time Mobile Manipulation Under Visual Scale Variation and Disturbances
May 1, 2026Xianbo Cai, Hideyuki Ichiwara, Hyogo Hiruma +3Mobile Manipulation Visuomotor Policy Learning
Department of Intermedia Art and Science, Waseda University, Tokyo, Japan · SB Intuitions Corp., Tokyo, Japan · Research and Development Group, Hitachi, Ltd., Ibaraki, Japan +1
Being-H0.7: A Latent World-Action Model from Egocentric Videos
Apr 30, 2026Hao Luo, Wanpeng Zhang, Yicheng Feng +6Visuomotor Policy Learning Efficient VLA Models
BeingBeyond Team
CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
Apr 27, 2026Fan Du, Feng Yan, Jianxiong Wu +8Robotic Control Efficient VLA Model Inference
1Southern University of Science and Technology · 2Xi’an Jiaotong University · 3United Nova Technology +1
FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception
Apr 23, 2026Zhen Zhang, Weinan Wang, Hejia Sun +4Dexterous Manipulation Visuomotor Policy Learning
The Chinese University of Hong Kong · The Hong Kong Polytechnic University
Gated Memory Policy
Apr 21, 2026Yihuai Gao, Jinyun Liu, Shuang Li +1Sequential Decision Making Visuomotor Policy Learning
Stanford University
R3D: Revisiting 3D Policy Learning
Apr 16, 2026Zhengdong Hong, Shenrui Wu, Haozhe Cui +83D ViTs Visuomotor Policy Learning
Zhejiang University · ShanghaiTech University
DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation
Apr 16, 2026Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3Mobile Manipulation Generalization in Robotic Manipulation
Beijing University of Posts and Telecommunications, Beijing, China · Nanyang Technological University, Singapore
VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning
Mar 18, 2026Tianxing Zhou, Feiyang Xue, Zhangchen Ye +3Visuomotor Policy Learning Robotic Manipulation
1IIIS, Tsinghua University · 2Galaxea 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
Foundation and Small Models Coordination for Visuomotor Policy Learning
Mar 9, 2026Haoran Ding, Liang Ma, Yaxun Yang +7Visuomotor Policy Learning Robot Policy Generalization
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
Mind the Gap: Rethinking I/O Design for Contact-Rich Visuomotor Policy Learning
Feb 9, 2026Cuijie Xu, Shurui Zheng, Zihao Su +7Contact-Rich Robotic Manipulation Robot Teleoperation
Department of Electronic Engineering, Tsinghua University · Qiuzhen College, Tsinghua University · School of Mechanical and Aerospace Engineering, Jilin University +2
Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies
Feb 6, 2026Fangyuan Wang, Peng Zhou, Jiaming Qi +4Efficient VLM Inference Visuomotor Policy Learning
The Hong Kong Polytechnic University · Great Bay University · Northeast Forestry University +2
PACT-WAM: Predicting Actions and Visual Foresight with Compact Temporal Encoding for Robot Manipulation
Feb 5, 2026Yushan Liu, Jingjing Fan, Shoujie Li +3World Models for Robotics Visuomotor Policy Learning
Tsinghua University · Nanyang Technological University · Xspark AI
ISS Policy : Scalable Diffusion Policy with Implicit Scene Supervision
Dec 17, 2025Wenlong Xia, Jinhao Zhang, Ce Zhang +6Robot Skill Learning Visuomotor Policy Learning
[cs.RO]
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies
Dec 5, 2025Zhiying Du, Bei Liu, Yaobo Liang +7Visuomotor Policy Learning Vision-Language-Action Models
1Fudan University · 2Microsoft Research Asia · 3Xi’an Jiaotong University +1
SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
Nov 11, 2025Rong Xue, Jiageng Mao, Mingtong Zhang +1Rectified Flow Visuomotor Policy Learning
University of Southern California
Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation
Oct 30, 2025Georgios Kamaras, Craig Innes, Subramanian RamamoorthySimulation-Based Inference Adaptive Inference
School of Informatics, The University of Edinburgh, EH8 9AB, UK. · Heriot-Watt University · UKRI Turing AI World Leading Researcher Fellowship on AI for Person-Centred and Teachable Autonomy
eVGGT: An Efficient Geometry-Aware Vision Encoder for Visuomotor Policies
Sep 19, 2025An Dinh Vuong, Minh Nhat Vu, Ian ReidVisual Representation Learning Visuomotor Policy Learning
Department of Computer Vision, Mohammed bin Zayed University of Artificial Intelligence, UAE · AIT Austrian Institute of Technology GmbH, Austria
Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
Aug 8, 2025Zhanyi Sun, Shuran SongVisuomotor Policy Learning Diffusion Policy
Stanford University
Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Jul 21, 2025Ian Chuang, Jinyu Zou, Andrew Lee +2Visuomotor Policy Learning Efficient ViTs
University of California, Berkeley · 2Tongji University · University of California, Davis
Wavelet Policy: Imitation Learning in the Scale Domain with World Prior Memory
Apr 7, 2025Changchuan Yang, Haoxuan Xu, Yuhang Dong +3Latent Action Learning Visuomotor Policy Learning
Zhejiang University · The Hong Kong University of Science and Technology · Tsinghua University, DISCOVER Robotics
FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning
Date pendingHaihao Lin, Xiangsheng Huang, Xiao Yang +7Representation Learning VLM Adaptation
University of Chinese Academy of Sciences · Hebei Key Laboratory of Cognitive Intelligence, Xiong’an Institute of Innovation · Hebei University of Technology +1

May 11, 2026, cs.AIJ/K move · Enter open · S save

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Boyang Shen, Kaixiang Yang, Hao Wang, Qiuyu Yu+3

1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)

Efficient VLM Inference Efficient VLA Model Inference Visuomotor Policy Learning Efficient VLA Models Vision-Language-Action Models Test-Time Adaptation of VLA Models+1

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Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction. However, robotic manipulation is composed of many frequent closed-loop spatial adjustments, for which excessive abstraction may waste computation and weaken low-level geometric cues essential for precise control. Existing early-exit strategies attempt to reduce computation by stopping at predefined layers or applying heuristic rules such as action consistency, but they do not directly answer when a representation is actually sufficient for action. In this paper, we present LoopVLA, a recurrent VLA architecture that jointly learns representation refinement, action prediction, and sufficiency estimation. LoopVLA iteratively applies a shared Transformer block to refine multimodal tokens, and at each iteration produces both a candidate action and a sufficiency score that estimates whether further refinement is necessary. By sharing parameters across iterations, LoopVLA decouples refinement from absolute layer indices and grounds sufficiency estimation in the evolving representation itself. Since sufficiency has no direct supervision, we introduce a self-supervised distribution alignment objective, where intermediate confidence scores are trained to match the relative action quality across refinement steps, thereby linking sufficiency learning to policy optimization signals. Experiments on LIBERO, LIBERO-Plus, and VLA-Arena show that LoopVLA pushes the efficiency-performance frontier of VLA policies, reducing parameters by 45% and improving inference throughput by up to 1.7 times while matching or outperforming strong baselines in task success.

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