9 papers
R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
Connor Mattson, Varun Raveendra, Ellen Novoseller +3
Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-ro…
Mil-SCORE: Benchmarking Long-Context Geospatial Reasoning and Planning in Large Language Models
Aadi Palnitkar, Mingyang Mao, Nicholas Waytowich +2
As large language models (LLMs) are applied to increasingly longer and more complex tasks, there is a growing need for realistic long-context benchmarks that require selective read…
CREW-WILDFIRE: Benchmarking Agentic Multi-Agent Collaborations at Scale
Jonathan Hyun, Nicholas R Waytowich, Boyuan Chen
Despite rapid progress in large language model (LLM)-based multi-agent systems, current benchmarks fall short in evaluating their scalability, robustness, and coordination capabili…
Human-Inspired Multi-Level Reinforcement Learning
Mingkang Wu, Devin White, Vernon Lawhern +2
Reinforcement learning (RL), a common tool in decision making, learns control policies from various experiences based on the associated cumulative return/rewards without treating t…
Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose +3
Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when mak…
Multi-RAG: A Multimodal Retrieval-Augmented Generation System for Adaptive Video Understanding
Mingyang Mao, Mariela M. Perez-Cabarcas, Utteja Kallakuri +3
To effectively engage in human society, the ability to adapt, filter information, and make informed decisions in ever-changing situations is critical. As robots and intelligent age…