Search Results for author:
Found 10 papers, 4 papers with code
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation.
Language-Driven Representation Learning for Robotics
First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite.
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception
Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling.
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
A novel disentangled shape and appearance database of priors is first learned to embed objects in their respective shape and appearance space.
3D Shape Reconstruction From A Single 2D Image
6D Pose Estimation
+3
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation.
Ranked #1 on
6D Pose Estimation using RGBD
on CAMERA25
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo
However, the RGB-D baseline only grasps 35% of the hard (e. g., transparent) objects, while SimNet grasps 95%, suggesting that SimNet can enable robust manipulation of unknown objects, including transparent objects, in unknown environments.
A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
We describe a mobile manipulation hardware and software system capable of autonomously performing complex human-level tasks in real homes, after being taught the task with a single demonstration from a person in virtual reality.
The Alexa Meaning Representation Language
This paper introduces a meaning representation for spoken language understanding.
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Symbolic models capture linguistic structure but have not scaled successfully to handle the diverse language produced by untrained users.
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World
LSP learns physical representations for both categorical ({``}blue,{''} {``}mug{''}) and relational ({``}on{''}) language, and also learns to compose these representations to produce the referents of entire statements.
