20 citations · 48 across the 21 of their papers we have counts for
9 papers · 1 filter
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
Jingkai Chen, Jiaoyang Li, Chuchu Fan +1
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and o…
Fast-reactive probabilistic motion planning for high-dimensional robots
Siyu Dai, Andreas Hofmann, Brian C. Williams
Many real-world robotic operations that involve high-dimensional humanoid robots require fast-reaction to plan disturbances and probabilistic guarantees over collision risks, where…
Helpfulness as a Key Metric of Human-Robot Collaboration
Richard G. Freedman, Steven J. Levine, Brian C. Williams +1
As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions. One such dimension is effectiveness: people wi…
Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
Allen Wang, Xin Huang, Ashkan Jasour +1
This paper presents fast non-sampling based methods to assess the risk of trajectories for autonomous vehicles when probabilistic predictions of other agents' futures are generated…
Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning
Allen Wang, Ashkan Jasour, Brian Williams
Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state. When nonlinear models are used, Gaussian assumptions on the state di…
Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles
Charles Dawson, Ashkan Jasour, Andreas Hofmann +1
Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning…