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cs.RO2025
Generalizable Reinforcement Learning with Biologically Inspired Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path Planning
Shay Snyder, Ryan Shea, Andrew Capodieci +2
Real-time autonomous systems utilize multi-layer computational frameworks to perform critical tasks such as perception, goal finding, and path planning. Traditional methods impleme…
cs.RO2024
Brain Inspired Probabilistic Occupancy Grid Mapping with Vector Symbolic Architectures
Shay Snyder, Andrew Capodieci, David Gorsich +1
Real-time robotic systems require advanced perception, computation, and action capability. However, the main bottleneck in current autonomous systems is the trade-off between compu…
cs.RO2023
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
Joey Wilson, Yuewei Fu, Joshua Friesen +5
In this paper, we develop a modular neural network for real-time {\color{black}(> 10 Hz)} semantic mapping in uncertain environments, which explicitly updates per-voxel probabilist…