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Interpretable Collision Avoidance and Feasibility Guarantees through an Uncertainty Aware Pipeline
Doris Xu, Jin Hyun Park, Guadalupe Alvarado Fernandez +2
Autonomous vehicles (AVs) operating in dynamic, challenging conditions such as intersections and roundabouts must guarantee collision avoidance under uncertainty while maintaining…
Learning Communication-Conditioned Generative Policies for Decentralized Multi-Agent Collision Avoidance
Prajwal Koirala, Mark Campbell
In this work, we propose a decentralized communication-conditioned generative framework for multi-agent collision avoidance. Agents generate short-horizon action sequences using a…
VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching
Prajwal Koirala, Mark Campbell
Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative models enable rich and multimod…
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning
Rohan Banerjee, Prishita Ray, Mark Campbell
Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving. However, an open probl…