Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot
Planning

v1v2v3v4 (latest)

Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning

2 February 2022

Siddharth Srivastava

ArXiv (abs)PDF HTML

Papers citing "Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning"

18 / 18 papers shown

Title
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions Constantinos Chamzas Zachary Kingston Carlos Quintero-Peña Anshumali Shrivastava Lydia E. Kavraki 90 39 0 29 Oct 2020
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning Rushang Karia Siddharth Srivastava 46 42 0 10 Jul 2020
Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms Matteo Luperto Luca Fochetta Francesco Amigoni 14 4 0 15 Apr 2020
Learning Domain-Independent Planning Heuristics with Hypergraph Networks William Shen Felipe W. Trevizan Sylvie Thiébaux 72 83 0 29 Nov 2019
Learned Critical Probabilistic Roadmaps for Robotic Motion Planning Brian Ichter Edward Schmerling T. Lee Aleksandra Faust 45 71 0 08 Oct 2019
LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning Rahul Kumar Aditya Mandalika Sanjiban Choudhury S. Srinivasa 76 60 0 22 Jul 2019
Anytime Integrated Task and Motion Policies for Stochastic Environments Naman Shah Deepak Kala Vasudevan Kislay Kumar Pranav Kamojjhala Siddharth Srivastava 51 28 0 30 Apr 2019
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images Taibou Birgui Sekou Moncef Hidane Julien Olivier H. Cardot MedIm 49 33 0 08 Apr 2019
Learn and Link: Learning Critical Regions for Efficient Planning Daniel Molina Kislay Kumar Siddharth Srivastava 63 28 0 08 Mar 2019
Learning Plannable Representations with Causal InfoGAN Thanard Kurutach Aviv Tamar Ge Yang Stuart J. Russell Pieter Abbeel GAN DRL 79 180 0 24 Jul 2018
Learning Implicit Sampling Distributions for Motion Planning Clark Zhang Jinwook Huh Daniel D. Lee 45 77 0 06 Jun 2018
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning Caelan Reed Garrett Tomás Lozano-Pérez L. Kaelbling 74 260 0 23 Feb 2018
Learning Sampling Distributions for Robot Motion Planning Brian Ichter James Harrison Marco Pavone 57 354 0 16 Sep 2017
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel GAN 161 4,240 0 12 Jun 2016
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 435 18,361 0 27 May 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.9K 77,520 0 18 May 2015
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification L. Hou Dimitris Samaras Tahsin M. Kurc Yi Gao James E. Davis Joel H. Saltz MedIm 74 780 0 29 Apr 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,433 0 22 Dec 2014

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.