Search Results for author: Johnny Lee

Found 9 papers, 6 papers with code

Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research

no code implementations21 Apr 2022 Ryan Hoque, Kaushik Shivakumar, Shrey Aeron, Gabriel Deza, Aditya Ganapathi, Adrian Wong, Johnny Lee, Andy Zeng, Vincent Vanhoucke, Ken Goldberg

Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware.

Imitation Learning

Implicit Behavioral Cloning

4 code implementations1 Sep 2021 Pete Florence, Corey Lynch, Andy Zeng, Oscar Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, Jonathan Tompson

We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models.

Spatial Action Maps for Mobile Manipulation

1 code implementation20 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

Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations

no code implementations9 Dec 2019 Shuran Song, Andy Zeng, Johnny Lee, Thomas Funkhouser

A key aspect of our grasping model is that it uses "action-view" based rendering to simulate future states with respect to different possible actions.

Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly

1 code implementation30 Oct 2019 Kevin Zakka, Andy Zeng, Johnny Lee, Shuran Song

This formulation enables the model to acquire a broader understanding of how shapes and surfaces fit together for assembly -- allowing it to generalize to new objects and kits.

Pose Estimation

ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

1 code implementation6 Oct 2019 Shreeyak S. Sajjan, Matthew Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, Shuran Song

To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation.

Depth Completion Monocular Depth Estimation +4

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

no code implementations27 Mar 2019 Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser

In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error.

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

4 code implementations27 Mar 2018 Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser

Skilled robotic manipulation benefits from complex synergies between non-prehensile (e. g. pushing) and prehensile (e. g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free.

Q-Learning reinforcement-learning

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