35 citations · 52 across the 3 of their papers we have counts for
5 papers
Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning
Lucas Manuelli, Yunzhu Li, Pete Florence +1
Predictive models have been at the core of many robotic systems, from quadrotors to walking robots. However, it has been challenging to develop and apply such models to practical r…
Self-Supervised Correspondence in Visuomotor Policy Learning
Peter Florence, Lucas Manuelli, Russ Tedrake
In this paper we explore using self-supervised correspondence for improving the generalization performance and sample efficiency of visuomotor policy learning. Prior work has prima…
kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation
Lucas Manuelli, Wei Gao, Peter Florence +1
We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. Existing manipulation pipelines typica…
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park, Peter Florence, Julian Straub +2
Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-o…
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
Pat Marion, Peter R. Florence, Lucas Manuelli +1
Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of thes…