5 papers · 1 filter
WAM-OPD: Joint Video-Action Supervision for World Action Model Post-Training with On-Policy Distillation
Liuhaichen Yang, Zhengyang Zhong, Zhuang Jiang +13
World Action Models (WAMs) generate both future video and robot actions, offering two connected outputs for post-training supervision. How can a pretrained WAM learn from a stronge…
Guided Action Flow: Value-Guided Sampling for Frozen Vision-Language-Action Policies
Liuhaichen Yang, Zhuang Jiang, Chenchao Sheng +8
Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves further updates to the policy parameters. F…
SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models
Ningwei Bai, Xinyu Tan, Harry Gardner +6
Vision-Language-Action (VLA) models enable robots to execute manipulation tasks from natural-language instructions grounded in visual observations. However, existing VLA interfaces…
SignVLA: A Gloss-Free Vision-Language-Action Framework for Real-Time Sign Language-Guided Robotic Manipulation
Xinyu Tan, Ningwei Bai, Harry Gardener +6
We present, to our knowledge, the first sign language-driven Vision-Language-Action (VLA) framework for intuitive and inclusive human-robot interaction. Unlike conventional approac…
Reinforced Attention Learning
Bangzheng Li, Jianmo Ni, Chen Qu +5
Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multi…