131 citations · 397 across the 37 of their papers we have counts for
6 papers · 1 filter
Iterative Reachability Estimation for Safe Reinforcement Learning
Milan Ganai, Zheng Gong, Chenning Yu +2
Ensuring safety is important for the practical deployment of reinforcement learning (RL). Various challenges must be addressed, such as handling stochasticity in the environments,…
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…
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
Niloofar Mireshghallah, Justus Mattern, Sicun Gao +2
With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machi…
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 Stabilization Control from Observations by Learning Lyapunov-like Proxy Models
Milan Ganai, Chiaki Hirayama, Ya-Chien Chang +1
The deployment of Reinforcement Learning to robotics applications faces the difficulty of reward engineering. Therefore, approaches have focused on creating reward functions by Lea…
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Chenning Yu, Qingbiao Li, Sicun Gao +1
Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to…