activity
20182021
most citedEuclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems

16 citations · 23 across the 6 of their papers we have counts for

collaborators

9 papers

cs.RO20213 cited

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…

cs.RO20202 cited

Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees

M. Asif Rana, Anqi Li, Dieter Fox +3

Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot. In this paper, we propose to solve multi-t…

cs.RO2020

Generalized Nonlinear and Finsler Geometry for Robotics

Nathan D. Ratliff, Karl Van Wyk, Mandy Xie +2

Robotics research has found numerous important applications of Riemannian geometry. Despite that, the concept remain challenging to many roboticists because the background material…

cs.RO2020

Optimization Fabrics

Nathan D. Ratliff, Karl Van Wyk, Mandy Xie +2

This paper presents a theory of optimization fabrics, second-order differential equations that encode nominal behaviors on a space and can be used to define the behavior of a smoot…

cs.RO202016 cited

Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems

Muhammad Asif Rana, Anqi Li, Dieter Fox +3

Robotic tasks often require motions with complex geometric structures. We present an approach to learn such motions from a limited number of human demonstrations by exploiting the…

cs.RO20201 cited

Taking Recoveries to Task: Recovery-Driven Development for Recipe-based Robot Tasks

Siddhartha Banerjee, Angel Daruna, David Kent +9

Robot task execution when situated in real-world environments is fragile. As such, robot architectures must rely on robust error recovery, adding non-trivial complexity to highly-c…