156 citations · 477 across the 10 of their papers we have counts for
11 papers
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
Anthony Simeonov, Ankit Goyal, Lucas Manuelli +5
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline…
MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare
Yann Labbé, Lucas Manuelli, Arsalan Mousavian +7
We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a reg…
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation
Mohit Shridhar, Lucas Manuelli, Dieter Fox
Transformers have revolutionized vision and natural language processing with their ability to scale with large datasets. But in robotic manipulation, data is both limited and expen…
CLIPort: What and Where Pathways for Robotic Manipulation
Mohit Shridhar, Lucas Manuelli, Dieter Fox
How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that e…
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…
Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation
Peter R. Florence, Lucas Manuelli, Russ Tedrake
What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnost…
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…
On Li-Yorke Measurable Sensitivity
Jared Hallett, Lucas Manuelli, Cesar E. Silva
The notion of Li-Yorke sensitivity has been studied extensively in the case of topological dynamical systems. We introduce a measurable version of Li-Yorke sensitivity, for nonsing…
Recurrent Partial Words
Francine Blanchet-Sadri, Aleksandar Chakarov, Lucas Manuelli +2
Partial words are sequences over a finite alphabet that may contain wildcard symbols, called holes, which match or are compatible with all letters; partial words without holes are…