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.

Latest papers

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

EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
Feb 18, 2026Ruijie Zheng, Dantong Niu, Yuqi Xie +12Robotic Manipulation Robot Learning
NVIDIA · University of California, Berkeley · University of Maryland
Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures
Feb 17, 2026Welf Rehberg, Mihir Kulkarni, Philipp Weiss +1Robotic RL Cross-Embodiment Robot Learning
Department of Engineering Cybernetics at the Norwegian University of Science and Technology, O.S. Bragstads Plass 2D, 7034, Trondheim, Norway
RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation
Feb 15, 2026Xinhua Wang, Kun Wu, Zhen Zhao +10Synthetic Data Augmentation Generalization in Robotic Manipulation
Beijing Innovation Center of Humanoid Robotics · Computation, Information and Technology, Technical University of Munich · City University of Hong Kong +3
Coupled Local and Global World Models for Efficient First Order RL
Feb 5, 2026Joseph Amigo, Rooholla Khorrambakht, Nicolas Mansard +1Model-Based RL World Models for Robotics
Machines in Motion Laboratory, New York University, USA · LAAS-CNRS, Universit´e de Toulouse, CNRS, Toulouse, France · Artificial and Natural Intelligence Toulouse Institute, Toulouse, France
Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation
Jan 27, 2026Kaipeng Fang, Weiqing Liang, Yuyang Li +5Robot Skill Learning Bimanual Robotic Manipulation
University of Electronic Science and Technology of China · Southwest Jiaotong University · Tongji University +1
ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation
Oct 2, 2025Wenye Yu, Jun Lv, Zixi Ying +3Robot Policy Adaptation Robot Imitation Learning
Shanghai Jiao Tong University · Shanghai Innovation Institute · Noematrix Ltd.
AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?
Oct 1, 2025Anukriti Singh, Kasra Torshizi, Khuzema Habib +3Robotic Manipulation Data-Efficient Robot Learning
Department of Computer Science, University of Maryland, College Park, MD, USA.
Synergistic Action and Style Priors for Learning Legged Locomotion
Oct 4, 2024Oliver Hausdörfer, Alexander von Rohr, Éric Lefort +1RL for Legged Locomotion Robot Policy Learning
Technical University of Munich, Germany; TUM School of Computation, Information and Technology, Department of Computer Engineering, Learning Systems and Robotics Lab; Munich Institute of Robotics and Machine Intelligence.
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation
Apr 2, 2024Carlos Plou, Ana C. Murillo, Ruben Martinez-CantinModel-Based RL Data-Efficient Robot Learning
All authors are with the Instituto de Investigación en Ingeniería de Aragón (i3A) and DIIS, University of Zaragoza

Feb 18, 2026J/K move · Enter open · S save

EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data

Ruijie Zheng, Dantong Niu, Yuqi Xie, Jing Wang+11

NVIDIA · University of California, Berkeley · University of Maryland

Robotic Manipulation Robot Learning Transfer Learning Long-Horizon Robotic Manipulation Cross-Embodiment Robot Learning Data-Efficient Robot Learning

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Human behavior is among the most scalable sources of data for learning physical intelligence, yet how to effectively leverage it for dexterous manipulation remains unclear. While prior work demonstrates human to robot transfer in constrained settings, it is unclear whether large scale human data can support fine grained, high degree of freedom dexterous manipulation. We present EgoScale, a human to dexterous manipulation transfer framework built on large scale egocentric human data. We train a Vision Language Action (VLA) model on over 20,854 hours of action labeled egocentric human video, more than 20 times larger than prior efforts, and uncover a log linear scaling law between human data scale and validation loss. This validation loss strongly correlates with downstream real robot performance, establishing large scale human data as a predictable supervision source. Beyond scale, we introduce a simple two stage transfer recipe: large scale human pretraining followed by lightweight aligned human robot mid training. This enables strong long horizon dexterous manipulation and one shot task adaptation with minimal robot supervision. Our final policy improves average success rate by 54% over a no pretraining baseline using a 22 DoF dexterous robotic hand, and transfers effectively to robots with lower DoF hands, indicating that large scale human motion provides a reusable, embodiment agnostic motor prior.