5 papers · 1 filter
SPARROW: Survival-POMCP for Adaptive Robot Routing, Observation, and Waiting
Hshmat Sahak, Aoran Jiao, Nicholas Rhinehart +1
Temporary obstacles that may block a robot's planned route create a sequential navigation problem: a robot must decide whether to wait for a blockage to clear, reroute, or acquire…
OSCAR: Obstacle Survival Curves for Adaptive Robot Navigation
Hshmat Sahak, Aoran Jiao, Nicholas Rhinehart +1
A mobile robot following a graph of known routes can make costly navigation errors when a temporary obstacle blocks a critical edge: waiting too long behind a parked cart wastes ti…
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
Mozhgan Pourkeshavarz, Tianran Liu, Nicholas Rhinehart
Simulation with realistic traffic agents is essential for validating autonomous driving systems. Existing data-driven simulators learn agent behavior from higher-level abstractions…
Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation
James R. Han, Mithun Vanniasinghe, Hshmat Sahak +2
Scaling Reinforcement Learning to in-the-wild social robot navigation is both data-intensive and unsafe, since policies must learn through direct interaction and inevitably encount…
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation
James R. Han, Hugues Thomas, Jian Zhang +2
How can a robot safely navigate around people with complex motion patterns? Deep Reinforcement Learning (DRL) in simulation holds some promise, but much prior work relies on simula…