Socially Aware Safe Navigation for Heterogeneous Multi-Agent Systems
Risk-aware navigation in uncertain, crowded environments with heterogeneous agents.
This project develops socially aware navigation methods that adapt safety and motion decisions to uncertain interactions among robots, pedestrians, and other moving agents. This work is conducted in collaboration with Toyota Research Institute of North America (TRINA) - AMRD.
Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
Risk-adaptive demo
Real-world robot demo