21 citations · 50 across the 6 of their papers we have counts for
4 papers · 1 filter
RMP2: A Structured Composable Policy Class for Robot Learning
Anqi Li, Ching-An Cheng, M. Asif Rana +4
We consider the problem of learning motion policies for acceleration-based robotics systems with a structured policy class specified by RMPflow. RMPflow is a multi-task control fra…
RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies
Ching-An Cheng, Mustafa Mukadam, Jan Issac +4
Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex nonlinear relationships…
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…
An Online Learning Approach to Model Predictive Control
Nolan Wagener, Ching-An Cheng, Jacob Sacks +1
Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learnin…