51 citations · 67 across the 3 of their papers we have counts for
5 papers
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
Kirill Zubov, Zoe McCarthy, Yingbo Ma +11
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical…
Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation
David V. Gealy, Stephen McKinley, Brent Yi +10
Robots must cost less and be force-controlled to enable widespread, safe deployment in unconstrained human environments. We propose Quasi-Direct Drive actuation as a capable paradi…
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Tianhao Zhang, Zoe McCarthy, Owen Jow +4
Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be…
Probabilistically Safe Policy Transfer
David Held, Zoe McCarthy, Michael Zhang +2
Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the…
Learning Deep Neural Network Policies with Continuous Memory States
Marvin Zhang, Zoe McCarthy, Chelsea Finn +2
Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning…