2 citations · 4 across the 5 of their papers we have counts for
5 papers
TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments
Zhiyu Huang, Yun Zhang, Johnson Liu +3
Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising…
CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning
Jiaxun Cui, Chen Tang, Jarrett Holtz +4
Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other. However, this communication is usually not human-understandable. Usin…
Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
Cheng Tang, Zhishuai Liu, Pan Xu
The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value func…
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu +3
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications,…
Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction
Cheng Ruei Tang, Jun Wei Hsieh, Shin You Teng
Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooper…