84 citations · 339 across the 34 of their papers we have counts for
30 papers · 1 filter
Learning to Optimize in Model Predictive Control
Jacob Sacks, Byron Boots
Sampling-based Model Predictive Control (MPC) is a flexible control framework that can reason about non-smooth dynamics and cost functions. Recently, significant work has focused o…
Learning Sampling Distributions for Model Predictive Control
Jacob Sacks, Byron Boots
Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics…
Motion Policy Networks
Adam Fishman, Adithyavairan Murali, Clemens Eppner +3
Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not onl…
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…
Leveraging Experience in Lazy Search
Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots +1
Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the…
Entropy Regularized Motion Planning via Stein Variational Inference
Alexander Lambert, Byron Boots
Many Imitation and Reinforcement Learning approaches rely on the availability of expert-generated demonstrations for learning policies or value functions from data. Obtaining a rel…