12 papers
Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation
Xinyao Qin, Junjie Lu, Kaixin Wang +7
Human demonstrations for robot imitation learning often contain mistakes and corrective behaviors, such as imprecise grasps, object misalignment, unstable contact, and repeated att…
What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models
Yuanfang Peng, Jingjing Fu, Chuheng Zhang +6
Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual shifts pose practical challeng…
UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
Junjie Lu, Xinyao Qin, Yuhua Jiang +6
Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distributions remains challenging. In pa…
Learning Additively Compositional Latent Actions for Embodied AI
Hangxing Wei, Xiaoyu Chen, Chuheng Zhang +5
Latent action learning infers pseudo-action labels from visual transitions, providing an approach to leverage internet-scale video for embodied AI. However, most methods learn late…
ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training
Rushuai Yang, Hecheng Wang, Zhichao Wu +11
We study how to improve large foundation vision-language-action (VLA) systems through human-in-the-loop reinforcement learning (RL) in real-world environments. A key challenge is l…
ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution
Hui Sun, Chang Xu, Haonan Xie +7
LLM-driven Anomaly Detection (AD) helps enhance the understanding and explanatory abilities of anomalous behaviors in Time Series (TS). Existing methods face challenges of inadequa…
Discover, Learn, and Reinforce: Scaling Vision-Language-Action Pretraining with Diverse RL-Generated Trajectories
Rushuai Yang, Zhiyuan Feng, Tianxiang Zhang +6
Scaling vision-language-action (VLA) model pre-training requires large volumes of diverse, high-quality manipulation trajectories. Most current data is obtained via human teleopera…
How Do VLAs Effectively Inherit from VLMs?
Chuheng Zhang, Rushuai Yang, Xiaoyu Chen +4
Vision-language-action (VLA) models hold the promise to attain generalizable embodied control. To achieve this, a pervasive paradigm is to leverage the rich vision-semantic priors…
Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Rushuai Yang, Hangxing Wei, Ran Zhang +8
Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalabil…
PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models
Jiansong Wan, Chengming Zhou, Jinkua Liu +14
Recent studies have explored pretrained (foundation) models for vision-based robotic navigation, aiming to achieve generalizable navigation and positive transfer across diverse env…
villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models
Xiaoyu Chen, Hangxing Wei, Pushi Zhang +9
Vision-Language-Action (VLA) models have emerged as a popular paradigm for learning robot manipulation policies that can follow language instructions and generalize to novel scenar…
ARO: Large Language Model Supervised Robotics Text2Skill Autonomous Learning
Yiwen Chen, Yuyao Ye, Ziyi Chen +2
Robotics learning highly relies on human expertise and efforts, such as demonstrations, design of reward functions in reinforcement learning, performance evaluation using human fee…