Robot Policy Learning

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Robot policy learning studies how robots can turn camera, depth, tactile, and proprioceptive inputs into actions for tasks such as manipulation, locomotion, and navigation. Common approaches include behavior cloning from demonstrations, reinforcement learning in simulation or on hardware, and diffusion, flow-matching, or vision-language-action policies that generate action sequences; world models and hierarchical planners can add foresight and long-horizon structure. Researchers evaluate these methods in simulation suites such as LIBERO, CALVIN, RoboTwin, and navigation benchmarks, as well as on physical robots, with growing attention to memory, contact, safety, and recovery from failure. A central tension is how to make policies generalize across tasks, robots, and changing conditions while remaining data-efficient, reactive, reliable, and fast enough to deploy.

Latest papers

377 papers85 papers in the last four weeks, up 47% on the four weeks before. 0.5% of all new papers.

Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
Jun 6, 2026Ziang Li, Dongzhou Cheng, Yibin Wang +5Robot Policy Learning World Models for Robotics
Wuhan University · Shanghai Innovation Institute · Southeast University +2
Spline Policy: A Structured Representation for Robot Policies
Jun 5, 2026Mengze Tian, Yiming Li, Sichao Liu +2Robot Policy Learning Robot Skill Learning
Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · Idiap Research Institute, Martigny, Switzerland
ActionMap: Robot Policy Learning via Voxel Action Heatmap
Jun 5, 2026Pei Yang, Hai Ci, Yanzhe Chen +3Robot Policy Learning Robot Skill Learning
1Show Lab, National University of Singapore · 2NVIDIA
AEGIS: A Backup Reflex for Physical AI
Jun 4, 2026Josef ChenRobot Policy Learning Long-Horizon Robotic Manipulation
KAIKAKU
Flow-based Policy Adaptation without Policy Updates
Jun 4, 2026Luzhe Sun, Jingtian Ji, Haoran Chen +2Robot Policy Learning Robot Policy Adaptation
Toyota Technological Institute at Chicago · Stony Brook University
Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
Jun 4, 2026Nikolai Dorofeev, Alexey Odinokov, Rostislav YavorskiyRobot Policy Learning Adversarial Scenario Generation
SafePI.ai Grenoble, France · SafePI.ai Belgrade, Serbia · SafePI.ai Madrid, Spain
TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
Jun 4, 2026Peizhuo Li, Hongyi Li, Mingfeng Fan +9Active Perception Humanoid Robot Locomotion
MarmotLab, National University of Singapore · Center of X-Mechanics, Zhejiang University · South China University of Technology
WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation
Jun 3, 2026Ning Yang, Yan Huang, Kaiwen Peng +9Visual Navigation Robot Policy Learning
1Nanjing University · Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences +4
MAD: Mapping-Aware World Models for Agile Quadrotor Flight
Jun 3, 2026Xinhong Zhang, Runqing Wang, Yunfan Ren +6Robot Policy Learning World Models for Robotics
State Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China · Zhongguancun Academy, Beijing 100094, China · School of Computer Science and Technology, Tongji University +2
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
GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation
Jun 2, 2026Xinhai Li, Xiaotao Zhang, Yuehao Huang +10Robot Policy Learning VLN Benchmarks
Institute of Artificial Intelligence, China Telecom · Shanghai Jiao Tong University · Zhejiang University +3
World-Task Factorization for Robot Learning
Jun 1, 2026Eduardo Sebastián, Adrian Pfisterer, Vito Mengers +2Robotic Control Robot Policy Learning
Department of Computer Science and Technology, University of Cambridge, United Kingdom · Robotics and Biology Laboratory, Technische Universität Berlin, Germany · Robotics and Biology Laboratory, Technische Universität Berlin +1
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
May 31, 2026Zemin Yang, Yaoyu He, Yiming Zhong +5Robot Policy Learning Imitation Learning
ShanghaiTech University · Shanghai Jiao Tong University · The Chinese University of Hong Kong +1
Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation
May 31, 2026Zijia Chen, Yuenan Hou, Xinhua Jiang +3Robot Policy Learning Mixture of Experts
College of Electronic Science and Technology, National University of Defense Technology Changsha, 410073, China · Shanghai AI Laboratory Shanghai, 200000, China
Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
May 30, 2026Kyle Morgenstein, Bharath Masetty, Stephen Welch +1Robot Policy Learning Robotics Simulation
Apptronik · University of Texas at Austin
From Noise to Control: Parameterized Diffusion Policies
May 29, 2026Renhao Zhang, Haotian Fu, Mingxi Jia +3Robot Policy Learning Robot Policy Adaptation
University of Massachusetts · 2Brown University · 3Harvard
UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
May 28, 2026Yufei Jia, Zhanxiang Cao, Mingrui Yu +48Robot Policy Learning Robotics Simulation
MARS Policy: Multimodality Only When It Matters
May 28, 2026Jindou Jia, Tuo An, Yuxuan Hu +7Efficient Multimodal Inference Robot Policy Learning
1MARS Lab, Nanyang Technological University
Turning Video Models into Generalist Robot Policies
May 27, 2026Sizhe Lester Li, Evan Kim, Xingjian Bai +4Robot Policy Learning Cross-Embodiment Robot Learning
1MIT · 2Independent Researcher · 3CMU +1
Adversarial Dual On-Policy Distillation from Expressive Teacher
May 26, 2026Zhenglin Wan, Jingxuan Wu, Xingrui Yu +5Robot Policy Learning Adversarial Imitation Learning
School of Computing, National University of Singapore · Department of Statistics and Operations Research, UNC-Chapel Hill, America · CFAR, Agency for Science, Technology and Research, Singapore +4
A Factory-Floor Deployment Case Study of VLA Pipelines for Industrial Packaging Task: Workflow, Failures, and Lessons
May 25, 2026Brian Zhu, Philipp Schmitt, Philine Meister +12Robot Policy Learning Robotics
Siemens Corporation
ParkourFormer: Integrating Predictive Supervision and Sequence Modeling into Parkour Locomotion
May 25, 2026Yanheng Mai, Wenhao Xu, Zirui Huang +6Humanoid Robot Locomotion Robot Policy Learning
The Hong Kong University of Science and Technology (Guangzhou) · CLAI-LAB, CL-TECH · South China Agricultural University
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
Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
May 25, 2026Keyi Shen, Glen ChouRobotic Control Neural Network Verification
Georgia Institute of Technology, Atlanta, GA 30308
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
Neuro-Inspired Inverse Learning for Planning and Control
May 22, 2026Maryna Kapitonova, Tonio BallRobot Policy Learning Model-Based Planning
NeuroMentum AI · IMBIT, University of Freiburg, Germany
Point Tracking Improves World Action Models
May 22, 2026Jiarui Guan, Wenshuai Zhao, Yue Pei +3Point Tracking Robot Policy Learning
Aalto University · ELLIS Institute Finland · Beihang University +3
Semantically Structured Mixture-of-Experts for Compositional Robotic Manipulation
May 22, 2026Chengyu Deng, Guanqi Chen, Yizhou Chen +4Robot Policy Learning Robot Skill Learning
The University of Hong Kong · Southern University of Science and Technology
PGDG: Physically Grounded Data Generation for Robust Bimanual Policy Learning from a Single Demonstration
May 20, 2026Cunxi Dai, Haoran Chang, Aditya Nisal +5Robot Policy Learning Bimanual Robotic Manipulation
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Dexmate, USA

Jun 6, 2026J/K move · Enter open · S save

Light-WAM: Efficient World Action Models with State-Fusion Action Decoding

Ziang Li, Dongzhou Cheng, Yibin Wang, Shiyue Wang+4

Wuhan University · Shanghai Innovation Institute · Southeast University+2

Robot Policy Learning World Models for Robotics Visuomotor Policy Learning Efficient Inference for World Action Models World Action Models

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World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy to encode task-relevant temporal structure in its representations. Current WAMs often rely on large-scale generative architectures that incur high training costs and inference latency, making them difficult to deploy as efficient closed-loop policies. We propose Light-WAM, a lightweight World Action Model for efficient robot manipulation. Specifically, it is built with a compact video backbone and performs future-video supervision in a downsampled latent space, reducing the cost of video co-training while retaining its benefits for representation learning. For action prediction, Light-WAM introduces the StateFusionActionExpert, which reads adapted states from multiple backbone layers, fuses them through learned-query pooling, and directly predicts action chunks in a single forward pass. This design provides an efficient interface between video backbone representations and robot actions, avoiding the need for heavy generative action experts. Experiments demonstrate that Light-WAM maintains strong performance on LIBERO and achieves usable multi-task performance on RoboTwin 2.0, while using only 0.44B trainable parameters. It also achieves 72.03ms inference latency with 4.1GiB peak GPU memory and improved training throughput.