269 citations · 448 across the 5 of their papers we have counts for
5 papers · 1 filter
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Tianhe Yu, Chelsea Finn, Annie Xie +4
Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning fr…
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…
One-Shot Visual Imitation Learning via Meta-Learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang +2
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured e…
PLATO: Policy Learning using Adaptive Trajectory Optimization
Gregory Kahn, Tianhao Zhang, Sergey Levine +1
Policy search can in principle acquire complex strategies for control of robots and other autonomous systems. When the policy is trained to process raw sensory inputs, such as imag…
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
Tianhao Zhang, Gregory Kahn, Sergey Levine +1
Model predictive control (MPC) is an effective method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters. However, application of MPC can…