12 papers
ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning
Qing Miao, Yiming Zhao, Jing Yang +5
Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models (LLMs), yet it remains limit…
HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation
Shuanghao Bai, Meng Li, Xinyuan Lv +14
Humans achieve complex manipulation through coordinated whole-body control, whereas most Vision-Language-Action (VLA) models treat robot body parts largely independently, making hi…
BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning
Ruiheng Wang, Shuanghao Bai, Haoran Zhang +2
While autoregressive (AR) Vision-Language-Action (VLA) models have demonstrated formidable reasoning capabilities in robotic tasks, their sequential decoding process often incurs h…
Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models
Shuanghao Bai, Jing Lyu, Wanqi Zhou +9
Vision-Language-Action (VLA) models benefit from chain-of-thought (CoT) reasoning, but existing approaches incur high inference overhead and rely on discrete reasoning representati…
Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance
Yuedi Zhang, Shuanghao Bai, Wanqi Zhou +4
Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the non-intrusive assistance as an…
Reshaping Action Error Distributions for Reliable Vision-Language-Action Models
Shuanghao Bai, Dakai Wang, Cheng Chi +8
In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies. Most existing VLA framewor…