11 citations · 19 across the 7 of their papers we have counts for
7 papers
Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling
Eric Chen, Travis Manderson, Nare Karapetyan +3
Autonomous underwater vehicles (AUVs) are increasingly used to survey coral reefs, yet efficiently locating specific coral species of interest remains difficult: target species are…
Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur +4
While pre-trained large-scale vision models have shown significant promise for semantic correspondence, their features often struggle to grasp the geometry and orientation of insta…
Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity
Jaedong Hwang, Zhang-Wei Hong, Eric Chen +3
Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards. To ad…
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building
Jaedong Hwang, Zhang-Wei Hong, Eric Chen +3
Animals and robots navigate through environments by building and refining maps of space. These maps enable functions including navigation back to home, planning, search and foragin…
Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models
Eric Chen, Cherie Ho, Mukhtar Maulimov +2
Autonomous offroad driving is essential for applications like emergency rescue, military operations, and agriculture. Despite progress, systems struggle with high-speed vehicles ex…
SmartChoices: Augmenting Software with Learned Implementations
Daniel Golovin, Gabor Bartok, Eric Chen +7
In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall…
Redeeming Intrinsic Rewards via Constrained Optimization
Eric Chen, Zhang-Wei Hong, Joni Pajarinen +1
State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e.g., -greedy) for exploration, but this method fails on hard exploration tasks like Monte…