27 citations · 95 across the 11 of their papers we have counts for
5 papers · 1 filter
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
Hongzhan Yu, Chiaki Hirayama, Chenning Yu +2
There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and th…
Patching Approximately Safe Value Functions Leveraging Local Hamilton-Jacobi Reachability Analysis
Sander Tonkens, Alex Toofanian, Zhizhen Qin +2
Safe value functions, such as control barrier functions, characterize a safe set and synthesize a safety filter, overriding unsafe actions, for a dynamic system. While function app…
Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation
Chenning Yu, Hongzhan Yu, Sicun Gao
Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices…
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
Chenning Yu, Sicun Gao
Sampling-based motion planning is a popular approach in robotics for finding paths in continuous configuration spaces. Checking collision with obstacles is the major computational…
Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics
Ya-Chien Chang, Sicun Gao
The lack of stability guarantee restricts the practical use of learning-based methods in core control problems in robotics. We develop new methods for learning neural control polic…