20 citations · 37 across the 10 of their papers we have counts for
11 papers
Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments
Charles Dawson, Ashkan Jasour, Andreas Hofmann +1
Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly im…
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Jingkai Chen, Jiaoyang Li, Yijiang Huang +7
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.…
Automatic Curricula via Expert Demonstrations
Siyu Dai, Andreas Hofmann, Brian Williams
We propose Automatic Curricula via Expert Demonstrations (ACED), a reinforcement learning (RL) approach that combines the ideas of imitation learning and curriculum learning in ord…
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…
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards
Siyu Dai, Wei Xu, Andreas Hofmann +1
In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with v…
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…
Fast Certification of Collision Probability Bounds with Uncertain Convex Obstacles
Charles Dawson, Andreas Hofmann, Brian Williams
To operate reactively in uncertain environments, robots need to be able to quickly estimate the risk that they will collide with their environment. This ability is important for bo…
Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments
Xin Huang, Sungkweon Hong, Andreas Hofmann +1
A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other…
Chance Constrained Motion Planning for High-Dimensional Robots
Siyu Dai, Shawn Schaffert, Ashkan Jasour +2
This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertain…
Improving Trajectory Optimization using a Roadmap Framework
Siyu Dai, Matthew Orton, Shawn Schaffert +2
We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates…
Moment-Sum-Of-Squares Approach For Fast Risk Estimation In Uncertain Environments
Ashkan Jasour, Andreas Hofmann, Brian C. Williams
In this paper, we address the risk estimation problem where one aims at estimating the probability of violation of safety constraints for a robot in the presence of bounded uncerta…