Search Results for author: Michael Laskey

Found 14 papers, 5 papers with code

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo

1 code implementation30 Jun 2021 Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan, Mark Tjersland

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.

Keypoint Detection Object +5

Untangling Dense Knots by Learning Task-Relevant Keypoints

no code implementations10 Nov 2020 Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Jeffrey Ichnowski, Ashwin Balakrishna, Minho Hwang, Vainavi Viswanath, Michael Laskey, Joseph E. Gonzalez, Ken Goldberg

HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97. 9% of 378 simulation experiments with an average of 12. 1 actions per trial.

A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes

no code implementations30 Sep 2019 Max Bajracharya, James Borders, Dan Helmick, Thomas Kollar, Michael Laskey, John Leichty, Jeremy Ma, Umashankar Nagarajan, Akiyoshi Ochiai, Josh Petersen, Krishna Shankar, Kevin Stone, Yutaka Takaoka

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.


Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning

1 code implementation6 Nov 2018 Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg

In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality.

Imitation Learning

Learning Robust Bed Making using Deep Imitation Learning with DART

no code implementations7 Nov 2017 Michael Laskey, Chris Powers, Ruta Joshi, Arshan Poursohi, Ken Goldberg

Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions.


Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

no code implementations27 Mar 2017 Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, Ken Goldberg

To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.


DART: Noise Injection for Robust Imitation Learning

2 code implementations27 Mar 2017 Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg

One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.

Imitation Learning

Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations

no code implementations4 Oct 2016 Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg

Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.

Cannot find the paper you are looking for? You can Submit a new open access paper.