Jeffrey L. Krichmar — accepted AI-conference papers

2024

A Rapid Adapting and Continual Learning Spiking Neural Network Path Planning Algorithm for Mobile Robots

Harrison Espino, Robert Bain, Jeffrey L. Krichmar

RA-L 2024

Mapping traversal costs in an environment and planning paths based on this map are important for autonomous navigation. We present a neurorobotic navigation system that utilizes a Spiking Neural Network (SNN) Wavefront Planner and E-prop learning to concurrently map and plan paths in a large and com

Cited by 9 Source ↗Scholar ↗

2021

Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation

Jinwei Xing, Takashi Nagata, Kexin Chen, Xinyun Zou, Emre Neftci, Jeffrey L. Krichmar

AAAI 2021technical

Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real-world applicability of Deep RL, zero-shot policy transfer is still a challenging problem since even minor visual change…

Cited by 54 Source ↗PDF ↗code ↗Scholar ↗