4 accepted papers
The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning and embodied physical action. These challenges require safety notions beyond physical constraint satisfaction. In this po…
We propose CARE (Collision Avoidance via Repulsive Estimation) for improving the robustness of learning-based visual navigation methods. Recently, visual navigation models, particularly foundation models, have demonstrated promising performance by generating viable trajectories using only RGB images…
We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive methods like Artificial Potential Field (APF) offer simplicity and efficiency, they suffer from local minima, causing robot
We propose a hybrid approach for decentralized multi-robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightweight deadlock prevention mechanism by forming temporary "roundabouts" (circular reference paths). Each robot relies only