5 papers
An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
Zhekai Duan, Kevin Ziyang Xie, Xinyu Tan +5
Adapting a pretrained Vision-Language-Action (VLA) model to a new robot, environment, or task requires demonstrations that are collected locally and often discarded. Federated lear…
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…
Fast ECoT: Efficient Embodied Chain-of-Thought via Thoughts Reuse
Zhekai Duan, Yuan Zhang, Shikai Geng +3
Embodied Chain-of-Thought (ECoT) reasoning enhances vision-language-action (VLA) models by improving performance and interpretability through intermediate reasoning steps. However,…
Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
Kaiwen Cai, Zhekai Duan, Gaowen Liu +2
Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotatio…