activity
20122023
most citedDeeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction

84 citations · 420 across the 49 of their papers we have counts for

collaborators
Showing 2020 · cs.ROShow all

7 papers · 2 filters

cs.RO2020★ 2 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★ 4 cited

Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation

Liyiming Ke, Jingqiang Wang, Tapomayukh Bhattacharjee +2

Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of everyday objects. The small, curved, and slippery tips of chopsticks pose a challenge for p…

cs.RO2020★ 18 cited

Stein Variational Model Predictive Control

Alexander Lambert, Adam Fishman, Dieter Fox +2

Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but rem…

cs.RO2020

Geometric Fabrics for the Acceleration-based Design of Robotic Motion

Mandy Xie, Karl Van Wyk, Anqi Li +5

This paper describes the pragmatic design and construction of geometric fabrics for shaping a robot's task-independent nominal behavior, capturing behavioral components such as obs…

cs.RO2020

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

Xingye Da, Zhaoming Xie, David Hoeller +5

We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The sy…

cs.RO2020★ 5 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…