Continual Learning for VLA Models

VLA: Vision-Language-Action

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models
Sep 30, 2026Mingyue Cui, Zheyuan Liu, Yihan Zhu +2Continual Learning for VLA Models Robotic Manipulation
University of Notre Dame
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
F4R: Failure-Driven Recognition, Reconstruction, Refinement, and Redeployment for Continual Robot Self-Improvement
Sep 28, 2026Zhuoyuan Yu, Jiacheng Wang, Tianle Liu +13Continual Robot Learning Sim-to-Real Transfer
Nanyang Technological University · Xi’an Jiaotong University · Dexmal
Brain-Inspired Hierarchical Modularity for General Continual Learning
Sep 21, 2026Hongwei Yan, Kanglei Zhou, Qi Cheng +7Continual Learning for LLMs Continual Learning for VLA Models
School of Life Sciences, Tsinghua University, Beijing, China. · IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China. · Tsinghua-Peking Center for Life Sciences, Beijing, China. +2
StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models
Aug 12, 2026Siyu Xu, Yunke Wang, Zijian Wang +6Continual Learning for VLA Models Robotic Manipulation
StellarEdge AI Technical Team
In-Context VLA: Endowing Vision-Language-Action Models with Language via In-Context Post-Training and Agentic Tool Use
Aug 6, 2026Jiarui Yang, Wen Huang, Jiale Zhang +2ICL for Robotics Continual Learning for VLA Models
Nankai University · Tsinghua University · Swiss Federal Institute of Technology in Lausanne (EPFL)
Towards Human-like Physical Intelligence: LifelongVision-Language-Action Learning for Robotic Manipulation
Jul 16, 2026Yao He, Gan Sun, Wenqi Liang +2Continual Robot Learning Robot Skill Learning
South China University of Technology · University of Trento
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
PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models
Jun 2, 2026Ziyang Chen, Shaoguang Wang, Weiyu Guo +5Continual Learning for VLA Models Robotic Manipulation
Thrust of AI, HKUST(Guangzhou), Guangzhou, China · AI2 Robotics, Shenzhen, China
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
Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT
May 9, 2026Tianyi Zhang, Shaopeng Zhai, Haoran Zhang +2Supervised Fine-Tuning Continual Learning for VLA Models
Shanghai Artificial Intelligence Laboratory
Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
May 2, 2026Wenhao Li, Xiu Su, Dan Niu +6Continual Learning for LLMs Robotics
University of Sydney · Central South University · Southeast University +2

Sep 30, 2026, cs.ROJ/K move · Enter open · S save

Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models

Mingyue Cui, Zheyuan Liu, Yihan Zhu, Zheyuan Zhang+1

University of Notre Dame

Continual Learning for VLA Models Robotic Manipulation Vision-Language-Action Models

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Vision-language-action (VLA) models generalize broadly across robotic manipulation tasks, but complex environments require balancing task success with unintended contact. Runtime shields can correct individual actions, but they leave the underlying policy unchanged, so repeated disagreements may create a persistent policy-shield mismatch that blocks task progress. To address this challenge, we introduce FailBank, a four-stage self-evolving framework that converts runtime feedback into persistent policy improvement. During collection, a fixed CBF-based safety module serves as an observe-only teacher, producing counterfactual corrections while the policy remains in control. Outcome-aware admission then converts useful proposals into corrective targets and retains successful uncorrected actions as quiet anchors for guarded LoRA updates. We evaluate FailBank on the VLA-Arena benchmark across two difficulty levels and two VLA backbones. Compared with the base policies, FailBank improves the joint success-cost operating point. Across the two backbones, FailBank improves task success rate by 8.5 and 6.9 percentage points, while reducing policy-induced cumulative cost by 35.6% and 23.8%, respectively. Compared with runtime shielding, FailBank raises task success rate by 25.4 and 9.5 percentage points, while maintaining comparable policy-induced cumulative cost. These results show that runtime feedback can serve as persistent policy supervision rather than only as a temporary action constraint.

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