10 papers
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…
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…
Multi-Robot Navigation in Social Mini-Games: Definitions, Taxonomy, and Algorithms
Rohan Chandra, Shubham Singh, Wenhao Luo +1
The "Last Mile Challenge" has long been considered an important, yet unsolved, challenge for autonomous vehicles, public service robots, and delivery robots. A central issue in thi…
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…
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…
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…
WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views
Andrew Jong, Mukai Yu, Devansh Dhrafani +4
We present the Wildland-fire Infrared Thermal (WIT-UAS) dataset for long-wave infrared sensing of crew and vehicle assets amidst prescribed wildland fire environments. While such a…