My research focus is on using continuous state partially observable Markov decision processes (POMDPs) to perform object manipulation tasks using a robotic arm. During object mani...
Partially observable Markov decision processes (POMDPs) have been
successfully applied to various robot motion planning tasks under uncertainty.
However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
Physically-based modeling has been used in the past to support a variety of interactive modeling tasks including free-form surface design, mechanism design, constrained drawing, a...
— Studies of human manipulation strategies suggest that pre-grasp object manipulation, such as rotation or sliding of the object to be grasped, can improve task performance by in...
Lillian Y. Chang, Siddhartha S. Srinivasa, Nancy S...
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...