128 citations · 133 across the 7 of their papers we have counts for
9 papers
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
Zifan Xu, Myoungkyu Seo, Dongmyeong Lee +8
Learning fast and robust ball-kicking skills is a critical capability for humanoid soccer robots, yet it remains a challenging problem due to the need for rapid leg swings, postura…
SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation
Michael J. Munje, Chen Tang, Shuijing Liu +6
Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-Language Models (VLMs) exhibit…
VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pokémon
Cameron Angliss, Jiaxun Cui, Jiaheng Hu +2
Developing AI agents that can robustly adapt to varying strategic landscapes without retraining is a central challenge in multi-agent learning. Pokémon Video Game Championships (VG…
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
Caroline Wang, Arrasy Rahman, Benjamin Nativi +4
Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT). Existing methods often adopt a two-stage pipeline…
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…
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
Arrasy Rahman, Jiaxun Cui, Peter Stone
Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Team…
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles
Jiaxun Cui, Hang Qiu, Dian Chen +2
Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hin…
Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction
Yulin Zhang, William Macke, Jiaxun Cui +2
In most modern cities, traffic congestion is one of the most salient societal challenges. Past research has shown that inserting a limited number of autonomous vehicles (AVs) withi…