4 citations · 8 across the 5 of their papers we have counts for
6 papers
Game Theoretic Decision Making by Actively Learning Human Intentions Applied on Autonomous Driving
Siyu Dai, Sangjae Bae, David Isele
The ability to estimate human intentions and interact with human drivers intelligently is crucial for autonomous vehicles to successfully achieve their objectives. In this paper, w…
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…
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…