SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

2 code implementations • 5 Mar 2017 • Jay M. Wong, Vincent Kee, Tiffany Le, Syler Wagner, Gian-Luca Mariottini, Abraham Schneider, Lei Hamilton, Rahul Chipalkatty, Mitchell Hebert, David M. S. Johnson, Jimmy Wu, Bolei Zhou, Antonio Torralba

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios.

Object Recognition Point Cloud Registration +3

Expert identification of visual primitives used by CNNs during mammogram classification

1 code implementation • 13 Mar 2018 • Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance D. Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester Mackey, Genevieve Patterson

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms.

Classification General Classification

DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation

1 code implementation • 31 May 2018 • Jimmy Wu, Bolei Zhou, Diondra Peck, Scott Hsieh, Vandana Dialani, Lester Mackey, Genevieve Patterson

We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions.

Classification General Classification +1

Real-Time Object Pose Estimation with Pose Interpreter Networks

2 code implementations • 3 Aug 2018 • Jimmy Wu, Bolei Zhou, Rebecca Russell, Vincent Kee, Syler Wagner, Mitchell Hebert, Antonio Torralba, David M. S. Johnson

In this work, we introduce pose interpreter networks for 6-DoF object pose estimation.

Object Pose Estimation

Spatial Action Maps for Mobile Manipulation

1 code implementation • 20 Apr 2020 • Jimmy Wu, Xingyuan Sun, Andy Zeng, Shuran Song, Johnny Lee, Szymon Rusinkiewicz, Thomas Funkhouser

Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e. g., step forward, turn left, turn right, etc.)

Q-Learning Value prediction

DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with Dynamic Voxelization and 3D Group Convolution

no code implementations • 7 Sep 2020 • Zhaoyu Su, Pin Siang Tan, Junkang Chow, Jimmy Wu, Yehur Cheong, Yu-Hsing Wang

3D point cloud interpretation is a challenging task due to the randomness and sparsity of the component points.

Spatial Intention Maps for Multi-Agent Mobile Manipulation

1 code implementation • 23 Mar 2021 • Jimmy Wu, Xingyuan Sun, Andy Zeng, Shuran Song, Szymon Rusinkiewicz, Thomas Funkhouser

The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks.

Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower

1 code implementation • 5 Apr 2022 • Jimmy Wu, Xingyuan Sun, Andy Zeng, Shuran Song, Szymon Rusinkiewicz, Thomas Funkhouser

We investigate pneumatic non-prehensile manipulation (i. e., blowing) as a means of efficiently moving scattered objects into a target receptacle.

Metric-Fair Classifier Derandomization

no code implementations • 15 Jun 2022 • Jimmy Wu, Yatong Chen, Yang Liu

We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier $f: X \to [0, 1]$, sample a deterministic classifier $\hat{f}: X \to \{0, 1\}$ that approximates the output of $f$ in aggregate over any data distribution.

Fairness

TidyBot: Personalized Robot Assistance with Large Language Models

1 code implementation • 9 May 2023 • Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, Thomas Funkhouser

For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios.