Jeric Lew — accepted AI-conference papers

2025

DARE: Diffusion Policy for Autonomous Robot Exploration

Yuhong Cao, Jeric Lew, Jingsong Liang, Jin Cheng, Guillaume Sartoretti

ICRA 2025

Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences

Cited by 15 Source ↗code ↗Scholar ↗

2025

SALON: Self-supervised Adaptive Learning for Off-road Navigation

Matthew Sivaprakasam, Samuel Triest, Cherie Ho, Shubhra Aich, Jeric Lew, Isaiah Adu +2

ICRA 2025

Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a

Cited by 10 Source ↗Scholar ↗