activity
20152020
most citedModel-Based Generalization Under Parameter Uncertainty Using Path Integral Control

32 citations · 76 across the 11 of their papers we have counts for

collaborators

17 papers

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.RO20205 cited

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…

cs.RO2020

An Interior Point Method Solving Motion Planning Problems with Narrow Passages

Jim Mainprice, Nathan Ratliff, Marc Toussaint +1

Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, tradition…

cs.RO202032 cited

Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control

Ian Abraham, Ankur Handa, Nathan Ratliff +3

This work addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy form…

cs.RO2020

Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning

Michelle A. Lee, Carlos Florensa, Jonathan Tremblay +4

Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the…

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…