Search Results for author:
Found 14 papers, 9 papers with code
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
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
+4
NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference.
Ranked #1 on
Generalizable Novel View Synthesis
on NERDS 360
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning; adoption that has fueled a wealth of new models such as LLaVa, InstructBLIP, and PaLI-3.
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.
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.
Language models scale reliably with over-training and on downstream tasks
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
A Critical Evaluation of AI Feedback for Aligning Large Language Models
RLAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (RL), using feedback from a critic model.
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.
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.
The Alexa Meaning Representation Language
This paper introduces a meaning representation for spoken language understanding.
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.
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.
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception
Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling.
