Luis E. Navarro-Serment

Efficient Active Search Via Amortized Path-Integral Policies

This work presents amortized path-integral policies that enable efficient and real-time active search for robotic systems. We model search as an active sensing problem where agents select actions to maximize information about target locations. Unlike previous approaches that only consider informatio…

TADPO: Reinforcement Learning Goes Off-Road

Robot Navigation from Human Demonstration: Learning Control Behaviors

When working alongside human collaborators in dynamic environments such as a disaster recovery, an unmanned ground vehicle (UGV) may require fast field adaptation to perform its duties or learn novel tasks. In disaster recovery situations, personnel and equipment are constrained, so training must be…