84 citations · 349 across the 38 of their papers we have counts for
9 papers · 1 filter
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…
Safe Reinforcement Learning Using Advantage-Based Intervention
Nolan Wagener, Byron Boots, Ching-An Cheng
Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the developmen…
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…
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories
Mandy Xie, Anqi Li, Karl Van Wyk +3
Imitation learning (IL) is a frequently used approach for data-efficient policy learning. Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distribution…
The Value of Planning for Infinite-Horizon Model Predictive Control
Nathan Hatch, Byron Boots
Model Predictive Control (MPC) is a classic tool for optimal control of complex, real-world systems. Although it has been successfully applied to a wide range of challenging tasks…
STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation
Mohak Bhardwaj, Balakumar Sundaralingam, Arsalan Mousavian +4
Sampling-based model-predictive control (MPC) is a promising tool for feedback control of robots with complex, non-smooth dynamics, and cost functions. However, the computationally…