84 citations · 339 across the 36 of their papers we have counts for
14 papers · 1 filter
Expressiveness and Learning of Hidden Quantum Markov Models
Sandesh Adhikary, Siddarth Srinivasan, Geoff Gordon +1
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov m…
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
Ajay Mandlekar, Fabio Ramos, Byron Boots +4
Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline l…
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
Mustafa Mukadam, Ching-An Cheng, Dieter Fox +2
RMPflow is a recently proposed policy-fusion framework based on differential geometry. While RMPflow has demonstrated promising performance, it requires the user to provide sensibl…
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods
Ching-An Cheng, Xinyan Yan, Byron Boots
Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods ar…
Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inference
Keshav Kolur, Sahit Chintalapudi, Byron Boots +1
Efficient planning in dynamic and uncertain environments is a fundamental challenge in robotics. In the context of trajectory optimization, the feasibility of paths can change as t…
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…