Data-Efficient Robot Learning

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Data-efficient robot learning studies how robots can learn useful skills without costly collections of demonstrations or extensive trial and error on physical hardware. Common approaches include imitation learning, sample-efficient reinforcement learning, world models that support planning or simulated practice, and methods that select, augment, or reuse demonstrations. Human videos, pretrained vision-language-action models, and transfer across robot embodiments can provide useful starting points, while benchmarks such as LIBERO and RoboCasa test manipulation performance in simulation and on real robots. A central challenge is that gains on offline prediction or simulated tasks do not always translate to reliable real-world behavior, and adaptation must improve new skills without losing old ones or requiring excessive supervision.

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189 papers51 papers in the last four weeks, up 113% on the four weeks before. 0.3% of all new papers.

Robotic Data Inoculation
Oct 28, 2026Qiyang Li, Zhiyuan Zhou, Charles Xu +1Robotic Data Collection Data-Efficient Robot Learning
UC Berkeley
GAWM: A Compact Latent World-Action Model for Multi-Task Robot Manipulation
Oct 27, 2026Yang Liu, Xiaoli Liu, Liuzhao Li +3World Action Models Latent World Models
Genisom AI, Beijing, China
HumanoidNDM: Closing the Training-Deployment Gap of Motion Tracking via Humanoid Neural Dynamics Model
Oct 25, 2026Chao Chen, Zhenrong Wang, Jilong Wang +4Robot Policy Adaptation Humanoid Motion Tracking
CFCS, School of Computer Science, Peking University · Galbot · Institute of Automation, Chinese Academy of Sciences +2
Move, don't think: sample-efficient collision avoidance with lossy proximity sensors
Oct 22, 2026Carson Kohlbrenner, Niraj Pudasaini, William Xie +3Robot Collision Avoidance Data-Efficient Robot Learning
Human Interaction and Robotics Group, University of Colorado Boulder · Correll Lab, University of Colorado Boulder
Learning Robot Behavior Priors from Non-Robotic Synthetic Data
Oct 22, 2026Mateusz Odrowaz-Sypniewski, Michael Amir, Jasmine Bayrooti +2Synthetic Data Pretraining Data-Efficient Robot Learning
Department of Computer Science and Technology, University of Cambridge, UK
RangeNav: Task-Aligned LiDAR Range-Image Pretraining for Robot Navigation
Oct 21, 2026Samuel Foo Enze, Manthan Patel, Fan Yang +4Robot Navigation 3D Representation Learning
Robotic Systems Lab (RSL), ETH Zurich, Switzerland · NVIDIA
Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration
Oct 20, 2026Matthew Strong, Ankush K. Dhawan, Julien Poffet +3Robot Learning from Video Synthetic Data Generation for Robotics
Department of Computer Science, Stanford University, Stanford, CA, USA · Department of Mechanical Engineering, Stanford University, Stanford, CA, USA · D-MAVT, ETH Zurich, Switzerland; work done while visiting Stanford University
Learning Tactile-Aware Hand Representations from Human Hand-Object Interaction Data
Oct 20, 2026Yeonguk Yu, Jaehong Kim, Byungok HanRobotic Tactile Sensing Data-Efficient Robot Learning
The authors are with the Electronics and Telecommunications Research Institute (ETRI), Republic of Korea
A Head Camera Is (Almost) All You Need: An Electronics-Free UMI Glove Labelled and Observed by One Moving Camera
Oct 20, 2026Yosub Shin, Yuan Liu, Igor MolybogRobotic Data Collection Data-Efficient Robot Learning
University of Hawaii at Manoa · Independent
Data-Efficient Learning from Demonstration for Humanoid Robots via Feature-Level Perception and Prompt-Based Object Detection
Oct 19, 2026Keyhan Rayati, AliReza Beigy, Anoosheh Saadati +4Robot Skill Learning Learning from Demonstration
Human and Robot Interaction Lab, University of Tehran, Tehran, Iran · Intelligent Robotics Laboratory, Oakland University, Rochester, Michigan, USA
SteerAct: Unlocking Action Models through Few-Shot Temporal Steering
Oct 19, 2026Kaichen Zhou, Zhiyan Li, Chongkai Gao +11Data-Efficient Robot Learning Test-Time Adaptation of VLA Models
Massachusetts Institute of Technology · Harvard University · Stanford University +3
Energy-Score Stabilized Attentive Mixture-Density Policy: A Lightweight Approach to Multimodal Robot Motion
Oct 18, 2026Sachiya Fujita, Hideyuki Ichiwara, Shigeki Sugano +1Visuomotor Policy Learning Mixture Density Networks
All authors are with the Waseda University, Shinjuku, 169-8555 Tokyo, Japan.
Towards Reinforcement Learning for Space Robotics: Fast Training and Reliable Real-World Transfer
Oct 16, 2026Abhishek Naik, Michael Wu, Michael O'Sullivan +2RL for Robotics RL for Legged Locomotion
National Research Council Canada · University of Waterloo · University of Ottawa +1
Embodiment-Classifier Guidance: Transferring Frozen VLAs Across Robot Dynamics without Demonstrations
Oct 15, 2026Devin Han, Hershraj NiranjaniCross-Embodiment Transfer Data-Efficient Robot Learning
UC Berkeley
SPEAR: Selective Preference Elicitation with Adaptive Rewards for VLM-Guided Robotic Manipulation
Oct 15, 2026Zewen Wu, Ruizhe Liu, Pei Zhou +2Active Preference Learning Preference Learning
HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR, China. · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Oct 14, 2026Zhefei Gong, Hefeifei Jiang, Zehao Wang +1Mobile Manipulation Robotic RL
Northeastern University · University of California, Riverside · Georgia Institute of Technology
PACER: Process-Aware VLA Policy Learning from Human Corrections for Robotic Disassembly
Oct 13, 2026Chang Liu, Sibo Tian, Litian Gong +4Robot Policy Learning Robotic Manipulation
Texas A&M University · University of California, Riverside · Georgia Institute of Technology
Steertuning Policies from a Few Human Videos
Oct 13, 2026Billy Yan, Isaac Tu, Kelly Lee +8Robot Policy Adaptation Data-Efficient Robot Learning
New York University
DR-VLA: Learning Human-Like Quadruped Navigation from Egocentric Web Videos via Domain-Invariant Scene Representations
Oct 13, 2026Jun Tian, Yingjie Niu, Ruo Chen +2Quadruped Robot Navigation Robot Navigation
Deep Robotics · University of Illinois at Urbana-Champaign · Zhejiang University +2
RESTA: Time-Scaling Augmentation for Data-Efficient Robot Imitation Learning
Oct 13, 2026Yunpeng Jiang, Jianshu Hu, Han Fang +3Data-Efficient Robot Learning Robot Imitation Learning
Global College, Shanghai Jiao Tong University, Shanghai, China · Shanghai Institute of Physical Intelligence and Robotics (PAIR), Shanghai, China · Duke Kunshan University, Jiangsu, China
TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments
Oct 13, 2026Qingyu Lan, Zhihao Tian, Zifan Wang +1Humanoid Robot Control Cross-Embodiment Robot Learning
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
Value-Preserving Action Quantization
Oct 12, 2026Max Muchen Sun, Jueun Kwon, Benjamin Riviere +1Data-Efficient Robot Learning Robot Imitation Learning
Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA · Department of Mechanical and Aerospace Engineering and the Department of Computer Science, New York University, New York City, NY 10012, USA
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
Oct 9, 2026Zhiyuan Xiang, Peidong Liu, Mingyang Li +4Latent Action Learning Data-Efficient Robot Learning
Joy Future Academy, JD · School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · Tsinghua SIGS, Tsinghua University +1
RARA: Robust Policy Fine-Tuning through Retrieval-Anchored Representation Alignment for Robotic Manipulation
Oct 9, 2026Xuhui Kang, Sung-Wook Lee, Yan Zhuang +2Robot Policy Adaptation Robustness of VLA Models
University of Virginia
Evo-RL: Iterative Robot Reinforcement Learning from Selective Experience
Oct 9, 2026Kai Ye, Yifan Jin, Qianhang Luo +7Robotic RL Robot Learning
School of AI, Shanghai Jiao Tong University · Central South University · South China Normal University +1
Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations
Oct 9, 2026Jiachen Zhang, Yuanyuan Li, Shaqi LuoRobotic Manipulation Cross-Embodiment Robot Learning
Beijing Academy of Artificial Intelligence, Beijing, China.
CeDR-Nav: Cross-Embodiment Mapless Navigation via Offline Demonstration Recasting
Oct 9, 2026Xianghui Wang, Xinming Zhang, Yiwei Qian +4Robot Navigation Cross-Embodiment Robot Learning
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei, China +2
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control
Oct 8, 2026Octi Zhang, Mateo Guaman Castro, Patrick Yin +4Robotic RL Data-Efficient Robot Learning
University of Washington · NVIDIA
FOCUS: From Privileged States to RGB-D with Controlled Modality Switching and Representation Alignment
Oct 8, 2026Filip Grigorov, Kourosh Darvish, Nandita VijaykumarRobotic RL Visual RL
University of Toronto Canada
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

Oct 28, 2026J/K move · Enter open · S save

Robotic Data Inoculation

Qiyang Li, Zhiyuan Zhou, Charles Xu, Sergey Levine

UC Berkeley

Robotic Data Collection Data-Efficient Robot Learning Robotic RL

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Modern robot learning systems feed on high-quality demonstration data, and their performance hinges on coverage. Without good coverage, small errors compound and the policy can quickly go into out-of-distribution states from which it cannot recover. Yet there is no principled way of collecting high-coverage data, and data collection often relies on heuristic instructions to human operators. Purely online reinforcement learning (RL) explores at the cost of safety and sample efficiency, while DAgger-style interventions require the policy to fail by chance before a human can correct it. In this paper, we ask instead whether we can modify how the robot behaves during data collection to collect more useful data. We propose Data Inoculation (DI), a shared-control framework in which a human provide closed-loop teleoperation commands while a residual policy perturbs these commands. The residual policy is trained online, on the growing dataset, with the same reward-maximizing objective later used for policy extraction, so its perturbations steer data collection toward the deviations a policy trained on the data would make. These perturbations expose the robot to a wider range of states, while the human's continuous corrections provide recovery demonstrations, "inoculating" the dataset against the learner's future mistakes while the human remains in control. On three challenging real-world robotic tasks, DI consistently improves or matches the success rate of policies trained with both behavioral cloning and offline RL, showing DI as an effective data collection strategy for improving downstream policy performance.