Robot Policy Adaptation

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Robot policy adaptation studies how a trained controller can adjust when tasks, surroundings, sensors, or robot dynamics differ from training. Common approaches range from fine-tuning with demonstrations or online reinforcement learning to lightweight residual modules, latent or noise-space steering, and world-model-guided correction of a frozen policy. Work spans manipulation, locomotion, and navigation, with evaluation in simulation and on real robots using benchmarks such as LIBERO and RoboTwin. A central challenge is improving performance with little feedback while preserving skills, safety, and reliable behavior under distribution shifts.

Latest papers

175 papers57 papers in the last four weeks, up 148% on the four weeks before. 0.3% of all new papers.

Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
Jun 16, 2026Mingtong Zhang, Dhruv ShahRobot Policy Learning Robot Policy Adaptation
Princeton University
When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
Jun 15, 2026Negin Musavi, Gokul Puthumanaillam, Ruben Hernandez +2Non-Stationary RL Robot Policy Adaptation
Department of Aerospace Engineering, University of Illinois Urbana–Champaign, Champaign, IL 61820, USA
Inference-time Policy Steering via Vision and Touch
Jun 12, 2026Yilin Wu, Zilin Si, Zeynep Temel +2Robot Policy Learning Robot Policy Adaptation
Carnegie Mellon University
TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations
Jun 12, 2026Kevin Robledo, Matías I. Torres Galaz, Kumar Dixhant Rai +3Robot Policy Learning Robot Policy Adaptation
Department of Computer Science California State University, Northridge · Department of Mechanical Engineering California State University, Northridge
Steering Multirobot Behavior via Closed-Loop Affine Activation Editing
Jun 9, 2026Satyajeet Das, Darren Chiu, Shashank Hegde +1Robot Policy Learning Robot Policy Adaptation
Department of Computer Science University of Southern California Los Angeles, CA, USA
MODIP: Efficient Model-Based Optimization for Diffusion Policies
Jun 9, 2026Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome +1Robot Policy Learning Robot Policy Adaptation
Sorbonne Université, CNRS, ISIR, F-75005 Paris, France · Institut Universitaire de France (IUF)
Robotic Policy Adaptation via Weight-Space Meta-Learning
Jun 5, 2026Christian Bianchi, Siamak Yousefi, Alessio Sampieri +4Robot Policy Adaptation Robot Skill Learning
ItalAI · ItalAI and Sapeinza University of Rome
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
TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation
Jun 4, 2026Dongwon Son, Florian Shkurti, Jason Lee +3Robotic Control Synthetic-to-Real Domain Adaptation
KAIST · Allen Institute for AI · University of Toronto +1
SplitAdapter: Load-Aware Humanoid Loco-Manipulation via Factorized Adaptation
Jun 2, 2026Jeonguk Kang, Hanbyel Cho, Sanghyun Kang +1Synthetic-to-Real Domain Adaptation Humanoid Loco-Manipulation
Future Robot AI Group, Samsung Electronics
Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation
Jun 1, 2026Zhiming Xu, Weitao Zhou, Xianghui Pan +4Robot Policy Adaptation Latent Dynamics Modeling
School of Computer Science and Engineering, Tongji University, Shanghai, China · School of Vehicle and Mobility, Tsinghua University, Beijing, China · Simple AI, Beijing, China +1
Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
May 31, 2026Hikmet Simsir, Ozgur S. OguzRobot Policy Adaptation Reinforcement Learning
Department of Computer Engineering, Bilkent University, Ankara, Türkiye.
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
CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
May 21, 2026Wentian Wang, Chutong Wen, Hongxu Ma +6Robot Policy Adaptation Robot Skill Learning
Synthoid AI · HKU
Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning
May 19, 2026Dongjie Yu, Kun Lei, Zhennan Jiang +2Robot Policy Adaptation Reinforcement Learning
School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR. · Shanghai Qizhi Institute, Shanghai, China. · Shanghai Jiao Tong University, Shanghai, China. +2
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
May 12, 2026Matthew M. Hong, Jesse Zhang, Anusha Nagabandi +1Robot Policy Adaptation Robot Skill Learning
University of Washington · Amazon FAR
PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models
May 11, 2026Xinyu Guo, Bin Xie, Wei Chai +4VLA Model Adaptation Robot Policy Adaptation
Institute of Automation, Chinese Academy of Sciences · Dexmal · Nanjing University of Aeronautics and Astronautics +1
UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
May 11, 2026Junjie Lu, Xinyao Qin, Yuhua Jiang +6RL for Robotics Robot Policy Adaptation
University of Technology Sydney · Work conducted at Microsoft Research. · Tsinghua University +1
Guided Streaming Stochastic Interpolant Policy
May 11, 2026Puming Jiang, Meiyi Wang, Kelvin Lin +2Robot Policy Learning Robot Policy Adaptation
School of Computing, National University of Singapore
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
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
May 8, 2026Yanzhe Chen, Kevin Yuchen Ma, Qi Lv +4Robot Policy Adaptation VLM Adaptation
National University of Singapore · Institude for Information Research, A*STAR · Harbin Institute of Technology (Shenzhen).
Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training
Apr 23, 2026Yaxuan Li, Zhongyi Zhou, Yefei Chen +5Robot Policy Learning Robot Policy Adaptation
Current Robotics · Tsinghua University · Peking University +1
CART: Context-Aware Terrain Adaptation using Temporal Sequence Selection for Legged Robots
Apr 15, 2026Kartikeya Singh, Youngjin Kim, Yash Turkar +1Terrain-Aware Robot Locomotion Robot Locomotion
DRONES LAB, University at Buffalo, NY, USA
Sustainable Transfer Learning for Adaptive Robot Skills
Apr 8, 2026Khalil Abuibaid, Vinit Hegiste, Nigora Gafur +2Robot Policy Adaptation Cross-Embodiment Robot Learning
Chair of Machine Tools and Control System, RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany · Innovative Factory Systems, German Institute of Artificial Intelligence, 67663 Kaiserslautern, Germany · Chair of Machine Tools and Control Systems, RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany
Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
Apr 7, 2026Jiahua Ma, Yiran Qin, Xin Wen +5Visuomotor Policy Learning Robot Policy Adaptation
Sun Yat-sen University · Oxford University.
DC-Ada: Reward-Only Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teams
Apr 5, 2026Saad AlqithamiGradient-Free Optimization Robot Policy Adaptation
Department of Computer Science, Al-Baha University, Albaha 65779 Saudi Arabia
Learning Task-Invariant Properties via Dreamer: Enabling Efficient Policy Transfer for Quadruped Robots
Apr 3, 2026Junyang Liang, Yuxuan Liu, Yabin Chang +5Sim-to-Real Transfer Robot Policy Adaptation
Shenzhen University, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China
OpenGo: An OpenClaw-Based Robotic Dog with Real-Time Skill Switching
Apr 2, 2026Hanbing Li, Xuewei Cao, Zhiwen Zeng +3Robot Skill Learning Robot Policy Adaptation
University of Science and Technology of China
Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
Mar 23, 2026Junhyeok Rui Cha, Woohyun Cha, Jaeyong Shin +2Sim-to-Real Transfer Robotics Simulation
Department of Intelligence and Information, Graduate School of Convergence Science and Technology, Seoul National University, Republic of Korea · 1X Technologies. This work was conducted while the author was at Seoul National University · Department of Intelligence and Information, Graduate School of Convergence Science and Technology, ASRI, AIIS, Seoul National University, Republic of Korea, and Advanced Institute of Convergence Technology (AICT), Suwon, Republic of Korea. He is the corresponding author of this paper.
Evolving Embodied Intelligence: Graph Neural Network--Driven Co-Design of Morphology and Control in Soft Robotics
Mar 20, 2026Jianqiang Wang, Shuaiqun Pan, Alvaro Serra-Gomez +2Robot Morphology-Control Co-Design Robot Policy Adaptation
LIACS, Leiden University · School of Mechanical Engineering, Xi’an Jiaotong University · Department of Computer Science, Loughborough University

Jun 16, 2026J/K move · Enter open · S save

Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement

Mingtong Zhang, Dhruv Shah

Princeton University

Robot Policy Learning Robot Policy Adaptation

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Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we propose VERITAS, a generator-verifier framework for generalist robot policies for inference-time policy steering and self-improvement. We use a pre-trained generalist robot policy as a generator'' and pair it with a gradient-free visual verifier'' that evaluates actions at inference time. This framework enables inference-time steering that improves policy performance without additional training. We demonstrate that inference-time verification consistently outperforms vanilla generalists without training on additional demonstration data. Additionally, we demonstrate that the verified rollouts provide effective supervision for offline policy improvement: policies fine-tuned on verified self-generated trajectories achieve consistent performance gains. Notably, we find that post-training with verified rollouts achieves comparable efficiency to expert demonstrations, while requiring no human interventions. Our results highlight inference-time verification as a practical and scalable mechanism for improving robotic policies during deployment.