Data-Efficient Robot Learning

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25 papers in the last four weeks, up 400% on the four weeks before. 0.2% of all new papers.

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PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation
Oct 6, 2026Kun Song, Yiming Wang, Yilin Chen +5Robot Policy Adaptation Robotic RL
The University of Hong Kong · Shanghai Jiao Tong University · Xense Robotics +1
Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors
Oct 6, 2026Maximilian Mühlbauer, Arne Sachtler, Markus Knauer +4Robotic Manipulation Data-Efficient Robot Learning
Department of Computer Engineering, Technical University of Munich, Friedrich-Ludwig-Bauer-Str. 3, Garching, Germany. · German Aerospace Center (DLR), Robotics and Mechatronics Center (RMC), Münchener Str. 20, 82234 Weßling, Germany. · School of Computer Science, University of Leeds, Leeds, UK.
Task-Space Imitation Guidance for Efficient Reinforcement Learning
Oct 5, 2026Salar Asayesh, Hossein Darani, Todd Cao +2Reward Shaping Imitation Learning
Sanctuary AI, Canada
Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation
Oct 5, 2026Álvaro Díez, Fidel AznarVariational Autoencoders Domain Adaptation
Department of Computer Science and Artificial Intelligence, University of Alicante
Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Oct 5, 2026Lars Ankile, Perry Dong, Rohan Bhowmik +4Robotic Data Collection Robotic Manipulation
Stanford University
TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models
Oct 1, 2026Keisuke Shirai, Tomohiro Motoda, Hanbit Oh +5Data-Efficient Robot Learning Vision-Language-Action Models
AIST
Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?
Sep 30, 2026Zhihao Sun, Liu Liu, Xinjiang Wang +6Data-Efficient Robot Learning Robot Imitation Learning
Institute of Trustworthy Embodied AI, Fudan University · Horizon Robotics · Huazhong University of Science & Technology +1
Tactile Curiosity Drives Robot Interaction
Sep 30, 2026Klemens Iten, Alexander Proshkin, Bhavya Sukhija +4Robotic RL Data-Efficient Robot Learning
University of California, Berkeley United States · The University of Texas at Austin United States
Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model
Sep 30, 2026Zaijing Li, Rui Shao, Bing Hu +3Memory-Augmented VLMs Robot Skill Learning
Harbin Institute of Technology (Shenzhen) · Pengcheng Laboratory
EmbodiRSI: Recursive Self-Improvement for Data-Efficient Robot Adaptation
Sep 30, 2026Haoran Lang, Haotao Lu, Shiyu Sang +6Robot Policy Adaptation Robotic Data Collection
InstAdapt · ShanghaiTech University
BIND: Binding 3D Robot Actions to 2D Image Features
Sep 29, 2026Cameron Smith, Arsh Tangri, Vitor Guizilini +3Visuomotor Policy Learning Data-Efficient Robot Learning
University of Southern California · Toyota Research Institute
In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
Sep 29, 2026Minxing Li, Minghao Han, Weizhi Zhao +10ICL for Robotics Robotic Manipulation
NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA) · Amap, Alibaba Group
Rho: A Foundation for Efficiently Adaptable VLA Models
Sep 29, 2026Rho Team, Simran Bagaria, Daphne Chen +11Robot Policy Adaptation Bimanual Robotic Manipulation
Microsoft Research
Encore: Few-Shot Agentic Discovery of Manipulation Strategies
Sep 29, 2026Yifan Kang, Zihan Wang, Zhiwen Fan +1Robot Policy Learning Language-Conditioned Robot Manipulation
Massachusetts Institute of Technology · AI Open Source Foundation · Texas A&M University +1
ComManip: Overfitting Manipulation Policies to Comfortable Regions
Sep 29, 2026Yidan Lai, Xinyi Chen, Shuquan Man +1Visuomotor Policy Learning Robotic Manipulation
South China University of Technology, Guangzhou, China · Nanyang Technological University, Singapore
EquivDP3: A SIM(3)-Invariant Point-Cloud Encoder for Data-Efficient Humanoid Loco-Manipulation
Sep 29, 2026Abu Hanif Muhammad Syarubany, Chang D. YooHumanoid Loco-Manipulation Point Cloud Learning
School of Electrical Engineering Korea Advanced Institute of Science & Technology (KAIST) Daejeon, Republic of Korea
From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Sep 28, 2026Bangjun Wang, Longyan Wu, Yukun Wei +8Object-Centric World Models Robotic Tool Use
The University of Hong Kong · Shanghai Innovation Institute · Xiaomi EV
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Sep 28, 2026Yichao Liang, Amber Li, Dat Nguyen +13World Models for Robotics Residual Dynamics Learning
Basis Research Institute · University of Cambridge · Carnegie Mellon University +6
UMR: Universal Manipulation Representation
Sep 28, 2026Song Liu, Linyi Li, Yanshun Zhao +11Zero-Shot Robotic Manipulation Generalization in Robotic Manipulation
University of Science and Technology of China, Hefei, China. · Suzhou Artificial Intelligence Laboratory, Suzhou, China.
WB-WAM: Heterogeneous Body-Hand Pre-training for Humanoid Loco-Manipulation
Sep 28, 2026Chuan Qin, Shaoting Zhu, Siyuan Luo +4Humanoid Loco-Manipulation Multimodal Pretraining
IIIS, Tsinghua University, Beijing, China. · Xiong’an Institute of Artificial Intelligence, Xiong’an, China. · The University of Melbourne, Melbourne, Australia.
ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim
Sep 27, 2026Ivan Kapelyukh, Xiaohan Zhang, Stephen James +2Robotic RL Robotic Manipulation
Imperial College London · Robotics and AI Institute
Residual Denoising Enables Sample-Efficient Multi-Agent Coordination on Demand
Sep 26, 2026Dayi Dong, Maulik Bhatt, Aayushi Shrivastava +2Multi-Agent Coordination Robot Policy Adaptation
Department of Mechanical Engineering, University of California Berkeley, Berkeley, CA 94709, USA
Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation
Sep 24, 2026Mariia Iavorskaia, Christian Dietz, Sebastian Albrecht +1Contact-Rich Robotic Manipulation Human-in-the-Loop AI
Siemens AG, Research and Predevelopment, Germany. · Technical University of Munich, Germany.
Robo-Harness K1: Harnessing Robot-Use Agents via Perception Augmentation
Sep 24, 2026Zexi Li, Yehang Zhang, Wenqian Li +12Language-Conditioned Robot Manipulation Tool-Using Agents
The Chinese University of Hong Kong · Knowin AI · The Hong Kong University of Science and Technology (Guangzhou)
Generalizable Robotic Insertion with World Models
Sep 23, 2026Nicklas Hansen, Iretiayo Akinola, Yijie Guo +7Zero-Shot Robotic Manipulation World Models for Robotics
NVIDIA · University of California San Diego · Work completed during an internship at NVIDIA +1
Imperfection for Precision: Upcycling Imperfect Data for High-Precision Robotic Manipulation
Sep 22, 2026Hao Wei, Yang Liu, Chao Tang +7Robotic Manipulation Data-Efficient Robot Learning
Samsung Robotics eXperience · National University of Singapore · University of Illinois Urbana-Champaign
Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation
Sep 21, 2026Bingjia Huang, Xin Ding, Fu Chen +5ICL for Robotics Robot Policy Adaptation
Institute for AI Industry Research (AIR), Tsinghua University; Z-Trans AI
From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention
Sep 18, 2026Sichang Su, Benjamin Yang, Zhiyun Deng +4Robot Policy Adaptation Reinforcement Learning
UT Austin · Autel US · UC Berkeley
WorldContact: A Contact-Centric World Model for Scalable Robot Learning
Sep 17, 2026Caoliwen Wang, Mengdi Wang, Heng Zhang +7Robot Policy Adaptation World Models for Robotics
University of British Columbia · Westlake University · Style3D Research
How to Better Train VLAs: Lessons Learned From the REAL-I Challenge at ICRA 2026
Sep 12, 2026Jiaming Wang, Jizhuo Chen, Diwen Liu +11Robot Policy Adaptation Robotic Policy Evaluation

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

PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation

Kun Song, Yiming Wang, Yilin Chen, Tianyi Ding+4

The University of Hong Kong · Shanghai Jiao Tong University · Xense Robotics+1

Robot Policy Adaptation Robotic RL Diffusion Policy Data-Efficient Robot Learning Force-Controlled Robotic Manipulation

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Pretrained robotic policies can suffer substantial performance degradation under out-of-distribution (OOD) conditions encountered during deployment, motivating post-training through real-world interaction. However, reinforcement-learning (RL)-based post-training typically requires substantial environment interactions, a burden that is especially significant in manipulation, where each trial can be slow, costly, or destructive. Therefore, we present PEARS, a physics-prior-guided hybrid RL framework for sample-efficient online adaptation of pretrained policies with tactile feedback. After each episode, its physics-guided force reasoning (PFR) module uses physical priors encoded in a vision-language model (VLM) to diagnose failures from the visual outcome and tactile interaction history and update task-appropriate contact-force bounds. A high-frequency hybrid force-position controller then enforces these bounds during contact. Complementarily, tactile-conditioned diffusion steering reinforcement learning adjusts the latent noise of the frozen flow-matching policy to correct errors in free-space motion and contact timing without updating the base model. In simulation, PEARS improves success rates by 12.4-37.4 percentage points over the strongest per-task baselines. PEARS also reduces the number of interaction episodes required for a certain success threshold by up to 53.2% relative to the fastest baseline. In real-world experiments, PEARS achieves success rates of 95% on Whiteboard Erasing and 90% on Pipette Liquid Aspiration. These results show that combining the PFR module with policy steering can accelerate adaptation while reducing costly interactions. The project website is available at https://song-kun.github.io/pears.

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