20 citations · 25 across the 6 of their papers we have counts for
6 papers
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula
Boling Yang, Liyuan Zheng, Lillian J. Ratliff +2
Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving sc…
Hierarchical Control Strategy for Moving A Robot Manipulator Between Small Containers
Paolo Torrado, Boling Yang, Joshua Smith
In this paper, we study the implementation of a model predictive controller (MPC) for the task of object manipulation in a highly uncertain environment (e.g., picking objects from…
Improved Object Pose Estimation via Deep Pre-touch Sensing
Patrick Lancaster, Boling Yang, Joshua R. Smith
For certain manipulation tasks, object pose estimation from head-mounted cameras may not be sufficiently accurate. This is at least in part due to our inability to perfectly calibr…
Benchmarking Robot Manipulation with the Rubik's Cube
Boling Yang, Patrick E. Lancaster, Siddhartha S. Srinivasa +1
Benchmarks for robot manipulation are crucial to measuring progress in the field, yet there are few benchmarks that demonstrate critical manipulation skills, possess standardized m…
Motivating Physical Activity via Competitive Human-Robot Interaction
Boling Yang, Golnaz Habibi, Patrick E. Lancaster +2
This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical ex…
Contact-less manipulation of millimeter-scale objects via ultrasonic levitation
Jared Nakahara, Boling Yang, Joshua R. Smith
Although general purpose robotic manipulators are becoming more capable at manipulating various objects, their ability to manipulate millimeter-scale objects are usually very limit…