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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Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models
Aug 27, 2026Senqiao Yang, Chengyao Wang, Yuxin Chen +13Robot Foundation Models Cross-Embodiment Robot Learning
Teach and Grow: An Agent-Centered Architecture for General Robot Learning
Aug 17, 2026Chang Nie, Zhe Liu, Hesheng WangRobot Skill Learning Generalization in Robotic Manipulation
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, and the Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai 200240, China
Adaptation of Generalist Robot Policies with Minimal Data
Aug 11, 2026Shreyas Kowshik, Sreyas Venkataraman, Leo Wang +3RL for Robotics Robot Policy Adaptation
Carnegie Mellon University
Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
Aug 11, 2026Wenrui Bao, Tianyun Jiang, Zhiben Chen +3World Models for Robotics Bimanual Robotic Manipulation
University of Central Florida · AdventHealth
Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition
Aug 10, 2026Changhao Li, Yifang Zhang, Heng Zhang +6Reinforcement Learning Robotic RL
HHCM, Istituto Italiano di Tecnologia, Genoa, Italy · DIBRIS, University of Genova, Genova, Italy · Human-Robot Interfaces and Interaction Lab, Istituto Italiano di Tecnologia, Genova, Italy +1
BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation
Aug 5, 2026Peiyan Li, Yuze Zhu, Yixiang Chen +10Memory-Augmented VLMs Generalization in Robotic Manipulation
New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · FiveAges, Beijing, China +1
KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
Jul 27, 2026Yaping Li, Zhaxizhuoma, Qiaojun Yu +3Robot Skill Learning Generalization in Robotic Manipulation
The Chinese University of Hong Kong · Shanghai AI Laboratory · Shanghai Jiao Tong University
Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation
Jul 23, 2026Yu Qi, Zhang Ye, Xinyi Xu +6Robot Skill Learning Language-Conditioned Robot Manipulation
Northeastern University · NVIDIA
Robots Acquire Manipulation Skills in Seconds from a Single Human Video
Jul 22, 2026Guangyan Chen, Meiling Wang, Te Cui +9Continual Robot Learning Robot Skill Learning
Beijing Institute of Technology · X SQUARE ROBOT · Tsinghua University
Learning to Navigate with Minimal Parameters: Decomposing Visual Navigation Through Closed-Form Geometric Interfaces
Jul 13, 2026Edward Beng Wai Tan, Siew-Kei LamVisual Navigation Robot Policy Learning
College of Computing and Data Science, Nanyang Technological University, Singapore.
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
Jul 9, 2026Michael Murray, Daphne Chen, Simran Bagaria +7Latent Action Learning Robot Policy Learning
1Microsoft Research · University of Washington · 2Microsoft +1
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
Jul 2, 2026Yuwan Liu, Hongze Yu, Song Liu +5Reinforcement Learning Robot Skill Learning
National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China · PKU-PsiBot Joint Lab, Beijing, China +2
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
Jun 22, 2026Ulas Berk Karli, Tesca FitzgeraldContinual Robot Learning Robot Skill Learning
Department of Computer Science Yale University United States
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.
Task-Error Residual Learning for Real-Robot Five-Ball Juggling
Jun 15, 2026Kai Ploeger, Jan PetersRobotic Control Robot Skill Learning
Technical University of Darmstadt, Germany · German Research Center for AI (DFKI), Germany · Hessian Center for Artificial Intelligence (hessian.AI), Germany
ATHENA: Accelerated Multi-Task Heterogeneous Influence Functions for Robot Data Curation
Jun 15, 2026Tao Xu, Jiaxin Wang, Runhao Zhang +7Multi-Task Learning Data-Efficient Robot Learning
1Tongji University · 2Shanghai Innovation Institute · 3Xi’an Jiaotong University +1
EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
Jun 12, 2026Yue Xu, Mingtao Nie, Tianle Li +4Robot Skill Learning Robotic Data Collection
Shanghai Jiao Tong University · Beijing Institute for General Artificial Intelligence (BIGAI) · Shanghai Innovation Institute
ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration
Jun 8, 2026Yuying Zhang, Francesco Verdoja, Wenyan Yang +1Robot Skill Learning Robotic RL
School of Electrical Engineering Aalto University, Finland
SynthICL: Scalable In-context Imitation Learning with Synthetic Data
Jun 6, 2026Cheng Qian, Ruomeng Fan, Yifei Ren +2ICL for Robotics Robot Skill Learning
The Robot Learning Lab Imperial College London
SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
Jun 1, 2026Jaehyeon Son, Junhyun Kim, Kyle Kam +7Robot Skill Learning Generalization in Robotic Manipulation
Georgia Institute of Technology · Allen Institute for AI · Johns Hopkins University +1
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Jun 1, 2026Junjie Ye, Rong Xue, Basile Van Hoorick +6Robot Skill Learning Robotic Data Collection
1USC Physical Superintelligence (PSI) Lab · 2Toyota Research Institute
Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation
May 31, 2026Zijia Chen, Yuenan Hou, Xinhua Jiang +3Robot Policy Learning Mixture of Experts
College of Electronic Science and Technology, National University of Defense Technology Changsha, 410073, China · Shanghai AI Laboratory Shanghai, 200000, China
PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation
May 27, 2026Yutai Li, Shaohui Peng, Jiaming Guo +8Long-Horizon Robotic Manipulation Robot Skill Learning
State Key Lab of Processors, Institute of Computing Technology, CAS · Jiangsu Key Laboratory of AI for Industries, Institute of AI for Industries, CAS · University of Chinese Academy of Sciences +3
EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
May 25, 2026Perry Dong, Kuo-Han Hung, Tian Gao +2Robot Skill Learning Robotic RL
Stanford University
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
May 24, 2026Zhi Wang, Botao He, Kelin Yu +4Zero-Shot Learning Zero-Shot Robotic Manipulation
University of Maryland
Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network
May 21, 2026Sayan Mitra, Ege Yuceel, Noah Giles +1Robotic Control Diffusion Policy
University of Illinois Urbana-Champaign
How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning
May 16, 2026Bosung Kim, Ruiyi Wang, David Acuna +5Robot Policy Learning Robot Skill Learning
University of California, San Diego · 2NVIDIA
RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data
May 13, 2026Harold Haodong Chen, Sirui Chen, Yingjie Xu +2Robot Skill Learning Synthetic Data Generation for Robotics
1The Hong Kong University of Science and Technology (Guangzhou) · 2The Hong Kong University of Science and Technology
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
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
May 11, 2026Lianghao Luo, Xizhou Bu, Ruyan Liu +5Robot Skill Learning World Models
Morphi

Aug 27, 2026, cs.ROJ/K move · Enter open · S save

Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

Senqiao Yang, Chengyao Wang, Yuxin Chen, Zixuan Wang+12

Robot Foundation Models Cross-Embodiment Robot Learning Data-Efficient Robot Learning Vision-Language-Action Models

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Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale image-text data because embodied collection is costly and sparsely covers the physical world. This makes representation quality a central bottleneck: under a fixed robot-data budget, continued pre-training must turn limited trajectories into transferable visual-action knowledge rather than merely fit actions. We propose VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data before task-specific fine-tuning. VLAct preserves the broad VLM prior and encourages shared action semantics across embodiments through VLM-prior preservation, multi-head continuous action co-supervision, and a partially unified cross-embodiment action layout, while allowing task-specific action heads during fine-tuning. Across simulation, real-world, and unseen-embodiment transfer, VLAct consistently improves downstream performance under fixed fine-tuning protocols. On LIBERO-Plus and RoboTwin 2.0, VLAct surpasses industrial VLA systems including ABot-M0 and LingBot-VLA, achieving success rates of 82.6% and 92.5%. On RoboDojo, VLAct ranks sixth among all policies by success rate and outperforms all explicitly designated world-action model (WAM) entries on both metrics. Most notably, on RoboCasa-GR1, an unseen humanoid embodiment, VLAct using only 20% of downstream trajectories outperforms the full-data GR00T-N1.6 baseline. These results are obtained using fully open-source data and only a 16-GPU training setup, showing that representation-centric continued pre-training can deliver highly competitive performance under a modest compute budget and is an important independent axis of VLA progress beyond data scaling.

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