84 citations · 420 across the 49 of their papers we have counts for
7 papers · 2 filters
Learning to Optimize in Model Predictive Control
Jacob Sacks, Byron Boots
Sampling-based Model Predictive Control (MPC) is a flexible control framework that can reason about non-smooth dynamics and cost functions. Recently, significant work has focused o…
Learning Sampling Distributions for Model Predictive Control
Jacob Sacks, Byron Boots
Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics…
Motion Policy Networks
Adam Fishman, Adithyavairan Murali, Clemens Eppner +3
Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not onl…
Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing
Carolina Higuera, Siyuan Dong, Byron Boots +1
We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environmen…
Learning Semantics-Aware Locomotion Skills from Human Demonstration
Yuxiang Yang, Xiangyun Meng, Wenhao Yu +3
The semantics of the environment, such as the terrain type and property, reveals important information for legged robots to adjust their behaviors. In this work, we present a frame…
Learning Implicit Priors for Motion Optimization
Julen Urain, An T. Le, Alexander Lambert +3
In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization. EBMs are a set of neural networks that can represent expr…