Search Results for author:
Found 9 papers, 4 papers with code
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Masked Trajectory Models for Prediction, Representation, and Control
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making.
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency.
Sample-efficient Surrogate Model for Frequency Response of Linear PDEs using Self-Attentive Complex Polynomials
Linear Partial Differential Equations (PDEs) govern the spatial-temporal dynamics of physical systems that are essential to building modern technology.
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
A Learned Stereo Depth System for Robotic Manipulation in Homes
We present a passive stereo depth system that produces dense and accurate point clouds optimized for human environments, including dark, textureless, thin, reflective and specular surfaces and objects, at 2560x2048 resolution, with 384 disparities, in 30 ms.
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.
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation.
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.
