Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Herbert, Sylvia L."'
Autor:
Choi, Jason J., Lee, Donggun, Li, Boyang, How, Jonathan P., Sreenath, Koushil, Herbert, Sylvia L., Tomlin, Claire J.
Control invariant sets are crucial for various methods that aim to design safe control policies for systems whose state constraints must be satisfied over an indefinite time horizon. In this article, we explore the connections among reachability, con
Externí odkaz:
http://arxiv.org/abs/2310.17180
Racing demands each vehicle to drive at its physical limits, when any safety infraction could lead to catastrophic failure. In this work, we study the problem of safe reinforcement learning (RL) for autonomous racing, using the vehicle's ego-camera v
Externí odkaz:
http://arxiv.org/abs/2110.07699
This paper works towards unifying two popular approaches in the safety control community: Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs). HJ Reachability has methods for direct construction of value functions that provide safe
Externí odkaz:
http://arxiv.org/abs/2104.02808
Autor:
Chen, Mo, Herbert, Sylvia L., Hu, Haimin, Pu, Ye, Fisac, Jaime F., Bansal, Somil, Han, SooJean, Tomlin, Claire J.
Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to b
Externí odkaz:
http://arxiv.org/abs/2102.07039
Hamilton-Jacobi-Isaacs (HJI) reachability analysis is a powerful tool for analyzing the safety of autonomous systems. This analysis is computationally intensive and typically performed offline. Online, however, the autonomous system may experience ch
Externí odkaz:
http://arxiv.org/abs/1903.07715
Autor:
Bajcsy, Andrea, Herbert, Sylvia L., Fridovich-Keil, David, Fisac, Jaime F., Deglurkar, Sampada, Dragan, Anca D., Tomlin, Claire J.
Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navi
Externí odkaz:
http://arxiv.org/abs/1811.05929
In the pursuit of real-time motion planning, a commonly adopted practice is to compute a trajectory by running a planning algorithm on a simplified, low-dimensional dynamical model, and then employ a feedback tracking controller that tracks such a tr
Externí odkaz:
http://arxiv.org/abs/1808.00649
Autor:
Fisac, Jaime F., Bajcsy, Andrea, Herbert, Sylvia L., Fridovich-Keil, David, Wang, Steven, Tomlin, Claire J., Dragan, Anca D.
In order to safely operate around humans, robots can employ predictive models of human motion. Unfortunately, these models cannot capture the full complexity of human behavior and necessarily introduce simplifying assumptions. As a result, prediction
Externí odkaz:
http://arxiv.org/abs/1806.00109
Autor:
Fridovich-Keil, David, Herbert, Sylvia L., Fisac, Jaime F., Deglurkar, Sampada, Tomlin, Claire J.
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at hi
Externí odkaz:
http://arxiv.org/abs/1710.04731
Autor:
Herbert, Sylvia L., Chen, Mo, Han, SooJean, Bansal, Somil, Fisac, Jaime F., Tomlin, Claire J.
Fast and safe navigation of dynamical systems through a priori unknown cluttered environments is vital to many applications of autonomous systems. However, trajectory planning for autonomous systems is computationally intensive, often requiring simpl
Externí odkaz:
http://arxiv.org/abs/1703.07373