1 citations · 2 across the 12 of their papers we have counts for
16 papers
Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL
Dongsu Lee, Sooraj Sathish, Joonkyung Kim +2
We argue that cooperative multi-agent reinforcement learning (MARL) should treat test-time coordination as part of the problem definition rather than a deployment artifact. Our foc…
Overcoming Valid Action Suppression in Unmasked Policy Gradient Algorithms
Renos Zabounidis, Roy Siegelmann, Mohamad Qadri +3
In reinforcement learning environments with state-dependent action validity, action masking consistently outperforms penalty-based handling of invalid actions, yet existing theory…
SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
Renos Zabounidis, Yue Wu, Simon Stepputtis +4
LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs generate skills or reward functions for RL, but…
Theory of Mind Guided Strategy Adaptation for Zero-Shot Coordination
Andrew Ni, Simon Stepputtis, Stefanos Nikolaidis +3
A central challenge in multi-agent reinforcement learning is enabling agents to adapt to previously unseen teammates in a zero-shot fashion. Prior work in zero-shot coordination of…
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Fair Cooperation in Mixed-Motive Games via Conflict-Aware Gradient Adjustment
Woojun Kim, Katia Sycara
Multi-agent reinforcement learning in mixed-motive settings presents a fundamental challenge: agents must balance individual interests with collective goals, which are neither full…
Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
Benjamin Li, Shuyang Shi, Lucia Romero +7
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…
CARE: Enhancing Safety of Visual Navigation through Collision Avoidance via Repulsive Estimation
Joonkyung Kim, Joonyeol Sim, Woojun Kim +2
We propose CARE (Collision Avoidance via Repulsive Estimation) to improve the robustness of learning-based visual navigation methods. Recently, visual navigation models, particular…
Distributed Multi-robot Source Seeking in Unknown Environments with Unknown Number of Sources
Lingpeng Chen, Siva Kailas, Srujan Deolasee +3
We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the nu…
Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning
Muhan Lin, Shuyang Shi, Yue Guo +7
Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-…
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning
Woojun Kim, Katia Sycara
Overestimation arising from selecting unseen actions during policy evaluation is a major challenge in offline reinforcement learning (RL). A minimalist approach in the single-agent…
HiMemFormer: Hierarchical Memory-Aware Transformer for Multi-Agent Action Anticipation
Zirui Wang, Xinran Zhao, Simon Stepputtis +4
Understanding and predicting human actions has been a long-standing challenge and is a crucial measure of perception in robotics AI. While significant progress has been made in ant…
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning
Srujan Deolasee, Siva Kailas, Wenhao Luo +2
Informative path planning (IPP) is an important planning paradigm for various real-world robotic applications such as environment monitoring. IPP involves planning a path that can…
OffRIPP: Offline RL-based Informative Path Planning
Srikar Babu Gadipudi, Srujan Deolasee, Siva Kailas +3
Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource c…
Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation
Joonkyung Kim, Sangjin Park, Wonjong Lee +3
We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive met…
ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition
Samuel Li, Sarthak Bhagat, Joseph Campbell +4
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments. Inspired by the human capability to grasp such objects through intuiti…