Continual Robot Learning

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The field studies how robots can keep learning from new tasks and real-world experience without losing skills they already use. Common approaches include replaying stored or generated experiences, adding modular adapters or skill experts, reusing skill libraries, and adapting policies through online reinforcement learning or interaction feedback. Researchers evaluate these methods in simulated manipulation and locomotion benchmarks such as LIBERO, Meta-World, and CALVIN, as well as on physical robots facing new tasks, terrain, or dynamics. A central challenge is balancing fast adaptation with reliable retention: replay and modularity can help, but forgetting remains, and safe validation of deployment-time updates is still an open concern.

Latest papers

89 papers24 papers in the last four weeks, up 85% on the four weeks before. 0.1% of all new papers.

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
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
Jun 16, 2026Nethmi Jayasinghe, Diana Gontero, Amit Ranjan TrivediRobot Policy Learning Continual Robot Learning
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA.
Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
Jun 14, 2026Shuaike Zhang, Shaokun Wang, Haoyu Tang +2Continual Robot Learning Robot Skill Learning
Harbin Institute of Technology, Shenzhen · Shandong University · Shenzhen Loop Area Institute
AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning
Jun 9, 2026Hang Yin, Yinan Liang, Jiazhao Zhang +4RL for Robotics Continual Robot Learning
Tsinghua University, Beijing 100084, China · Galbot Robotics, Beijing, China · Peking University, Beijing 100871, China +1
PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making
May 31, 2026Rufeng Chen, Yue Chang, Xiaqiang Tang +2Continual Robot Learning Embodied Navigation
The Hong Kong University of Science and Technology (Guangzhou), Guangdong, China · Jilin University, Jilin, China
Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning
May 29, 2026Navin Sriram Ravie, Andrew Jong, Krrish Jain +4Continual Robot Learning Mobile Robotics
Department of Engineering Design, Indian Institute of Technology, Madras · Robotics Institute, Carnegie Mellon University
Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments
May 27, 2026Julia Hindel, Simon Bultmann, Houman Masnavi +2Continual Robot Learning Robot Traversability Estimation
Department of Computer Science, University of Freiburg, Germany.
Can VLA Models Learn from Real-World Data Continually without Forgetting?
May 26, 2026Jiarun Zhu, Yijun Hong, Xiaoquan Sun +5Continual Robot Learning Continual Learning for VLA Models
1HKU · 5SUSTech · 2INFIFORCE +3
Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning
May 17, 2026Hong SuRobot Policy Learning Continual Robot Learning
School of Computer Science, Chengdu University of Information Technology, Chengdu, China
An LLM-Driven Closed-Loop Autonomous Learning Framework for Robots Facing Uncovered Tasks in Open Environments
Apr 24, 2026Hong SuContinual Robot Learning Robot Skill Learning
School of Computer Science, Chengdu University of Information Technology, Chengdu, China.
Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Apr 15, 2026Jianzong Wang, Botao Zhao, Yayun He +2Self-Evolving Agents Robotic Manipulation
Ping An Technology (Shenzhen) Co., Ltd. Shenzhen, China
Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots
Apr 14, 2026Yifei Yan, Linqi YeContinual Robot Learning Robot Skill Learning
School of Future Technology, Shanghai University, No. 99 Shangda Road, Baoshan District, Shanghai, 200444, Shanghai, China
Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning
Apr 13, 2026Tongfei Bian, Mathieu Chollet, Tanaya GuhaHuman-Robot Interaction Social Robotics
University of Glasgow, United Kingdom
ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration
Apr 6, 2026Rongfeng Zhao, Xuanhao Zhang, Zhaochen Guo +4VLMs for Robotics Cross-Embodiment Robot Learning
Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China · National Key Laboratory of Autonomous Intelligent Unmanned Systems (Tongji University), Shanghai 201210, China · Frontiers Science Center for Intelligent Autonomous Systems, Shanghai 201210, China +1
Robust Embodied Perception in Dynamic Environments via Disentangled Weight Fusion
Apr 2, 2026Juncen Guo, Xiaoguang Zhu, Jingyi Wu +4Domain-Incremental Learning Continual Robot Learning
Fudan University · University of California, Davis
Evidence of an Emergent "Self" in Continual Robot Learning
Mar 25, 2026Adidev Jhunjhunwala, Judah Goldfeder, Hod LipsonContinual Robot Learning
Creative Machines Lab, Department of Mechanical Engineering, Columbia University, New York, NY · Creative Machines Lab, Department of Computer Science, Columbia University, New York, NY
KiRAS: Keyframe Guided Self-Imitation for Robust and Adaptive Skill Learning in Quadruped Robots
Mar 16, 2026Xiaoyi Wei, Peng Zhai, Jiaxin Tu +5Robot Skill Learning Quadruped Locomotion
College of Intelligent Robotics and Advanced Manufacturing, Fudan University, China. · Power China Huadong Engineering Corporation Limited, China.
Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
Mar 13, 2026Peiqi Yu, Philip Huang, Chaitanya Chawla +3Robotic Assembly Robot Task Planning
Department of Electrical and Computer Engineering, Carnegie Mellon University · Robotics Institute, Carnegie Mellon University
RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
Mar 12, 2026Ruiying Li, Yunlang Zhou, YuYao Zhu +15Long-Horizon Robotic Manipulation VLMs for Robotics
AgiBot, China · National University of Singapore · Shanghai Jiao Tong University, Shanghai 200240, China +1
CORAL: Scalable Multi-Task Robot Learning via LoRA Experts
Mar 10, 2026Yuankai Luo, Woping Chen, Tong Liang +1Robot Learning Continual Robot Learning
Frontier Robotics
ProgAgent:A Continual RL Agent with Progress-Aware Rewards
Mar 8, 2026Jinzhou Tan, Gabriel Adineera, Jinoh KimContinual RL Continual Robot Learning
University of California, San Diego, USA · Texas A&M University-Commerce, USA
AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots
Mar 8, 2026Likui Zhang, Tao Tang, Zhihao Zhan +9Robot Skill Learning VLMs for Robotics
Sun Yat-sen University · Yinwang Intelligent Technology Co. Ltd. · Peng Cheng Laboratory
Lifelong Language-Conditioned Robotic Manipulation Learning
Mar 5, 2026Xudong Wang, Zebin Han, Zhiyu Liu +5Language-Conditioned Robot Manipulation Continual Robot Learning
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences · North University of China +2
Self-adapting Robotic Agents through Online Continual Reinforcement Learning with World Model Feedback
Mar 4, 2026Fabian Domberg, Georg SchildbachRobot Policy Adaptation Continual Robot Learning
Autonomous Systems Lab (ASL), Institute for Electrical Engineering in Medicine, University of Lübeck, 23562 Lübeck, Germany
Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
Mar 4, 2026Huihan Liu, Changyeon Kim, Bo Liu +2Continual Robot Learning Continual Learning for VLA Models
The University of Texas at Austin · KAIST · Microsoft Superintelligence.
When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
Feb 25, 2026Jessie Yuan, Yilin Wu, Andrea BajcsyRobot Policy Adaptation VLMs for Robotics
Carnegie Mellon University
GeCo-SRT: Geometry-aware Continual Adaptation for Robotic Cross-Task Sim-to-Real Transfer
Feb 24, 2026Wenbo Yu, Wenke Xia, Weitao Zhang +1Sim-to-Real Transfer Robot Policy Adaptation
Beijing Forestry University, Beijing, China · Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · Beijing Key Laboratory of Research on Large Models and Intelligent Governance +1
Continually Evolving Skill Knowledge in Vision Language Action Model
Nov 22, 2025Yuxuan Wu, Guangming Wang, Zhiheng Yang +4Continual Learning for VLA Models Continual Robot Learning
Shanghai Jiao Tong University · Shanghai Innovation Institute · University of Cambridge +4
Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation
Sep 23, 2025Yuan Meng, Zhenguo Sun, Max Fest +4Code Generation for Robotics Long-Horizon Robotic Manipulation
School of Computation, Information and Technology Technical University of Munich Munich, Germany · Beijing Academy of Artificial Intelligence (BAAI) Beijing, China · The State Key Laboratory for Novel Software Technology Nanjing University Suzhou, China

Jun 22, 2026J/K move · Enter open · S save

RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models

Ulas Berk Karli, Tesca Fitzgerald

Department of Computer Science Yale University United States

Continual Robot Learning Robot Skill Learning Elastic Weight Consolidation Imitation Learning Continual Learning for VLA Models Data-Efficient Robot Learning+3

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Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for tasks where the policy performs poorly. This approach incurs several downsides: it requires the robot to fail before data collection is triggered, provides little guidance about which states require supervision, and wastes demonstrator effort on redundant parts of the task where the policy already performs well. In this paper, we propose an active, continual learning paradigm for VLAs. We demonstrate that active, uncertainty-guided data collection leads to more efficient fine-tuning than when using passively-collected demonstrations. However, we also find that fine-tuning only on actively-collected recovery data leads to catastrophic forgetting. We evaluate techniques for continual learning, including replay-based data mixing and elastic weight consolidation, and identify tradeoffs between plasticity to uncertainty-guided recovery data and retention of previously learned behaviors. Overall, our work contributes an empirical study of active continual learning for autoregressive VLAs, establishing that uncertainty-guided recovery demonstrations can improve adaptation efficiency while also revealing open challenges when targeted new data is incorporated into large robot policies.