Robot Policy Adaptation

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32 papers in the last four weeks, up 967% on the four weeks before. 0.3% of all new papers.

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Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
Jun 28, 2026Hao Wang, Jiuzhou Lei, Dayou Li +5Robot Policy Learning Robot Policy Adaptation
Texas A&M University · University of Wisconsin · Northwestern University +1
ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies
Jun 27, 2026Tzu-Hsiang Lin, Srinivas Shakkottai, Dileep Kalathil +1Robot Policy Adaptation Robot Failure Recovery
Texas A&M University
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Jun 25, 2026Raymond Yu, William Huey, Mustafa Mukadam +2RL for Robotics Robot Policy Adaptation
University of Washington · Amazon FAR
Continual Robot Policy Learning via Variational Neural Dynamics
Jun 25, 2026Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4Robot Policy Learning Robot Policy Adaptation
Robotics and Perception Group, University of Zurich
Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure
Jun 25, 2026Yiyuan Pan, Hanjiang Hu, Shangtao Li +2Robot Policy Adaptation Robot Task Planning
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models
Jun 24, 2026Kejing Wang, Toan Nguyen, Minh Hoang Nguyen +2Robot Policy Adaptation VLM Adaptation
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia · Air Force Research Laboratory USA
SPACE: Enabling Learning from Cross-Robot Data Toward Generalist Policies
Jun 23, 2026Haeone Lee, Byeongguk Jeon, Suchae Jeong +2Robotic Control Robot Policy Adaptation
1KAIST · 2Config · 3Yonsei University
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
Jun 17, 2026Calvin Luo, Chen Sun, Shuran SongRobot Policy Adaptation Robotic RL
1Stanford University · 2Brown University.
CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion
Jun 17, 2026Francisco Affonso, Matheus P. Angarola, Ana Luiza Mineiro +3Robot Policy Adaptation Terrain-Aware Robot Locomotion
University of Illinois Urbana-Champaign · University of São Paulo
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
1KAIST · *Work done during an internship at the Allen Institute for AI. · 2Allen Institute for AI +2
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 +4Robot Policy Adaptation Robot Skill Learning
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).

Jun 28, 2026, cs.ROJ/K move · Enter open · S save

Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering

Hao Wang, Jiuzhou Lei, Dayou Li, Bangya Liu+4

Texas A&M University · University of Wisconsin · Northwestern University+1

Robot Policy Learning Robot Policy Adaptation Robot Imitation Learning Policy Learning

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Behavior-cloned policies often learn multiple behavior modes from demonstration datasets, including modes that are unsafe or otherwise undesired at deployment. For example, a policy trained on diverse handover demonstrations may learn to pass a knife blade-first. Standard remedies such as data curation and inference-time steering either require access to the original demonstrations for full retraining or add substantial inference-time overhead. To address this gap, we propose MoRE(Mode Redirection), which redirects policy rollouts toward desired behavior modes through a short "uncloning" step. Specifically, MoRE distills the redirection signal from a temporary mode classifier into the policy weights to steer behavior. A retain loss balances this edit by preserving desired-mode competence, allowing the standalone policy to suppress unwanted modes with zero inference-time overhead. Across eight simulated and real-world tasks, MoRE improves the average deployment success rate (SR) by 44 percentage points over the original mixed-mode policy. Among all compared adaptation and steering baselines, MoRE achieves the strongest SR and approaches the filtered-data retraining reference, while preserving task competence and inference speed. MoRE also generalizes across robot policy backbones, including Diffusion Policy and the Pi0.5 VLA, diverse task categories, and real-world deployments.

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