Search Results for author: Kostas E. Bekris

Found 12 papers, 5 papers with code

A Survey on the Integration of Machine Learning with Sampling-based Motion Planning

no code implementations15 Nov 2022 Troy McMahon, Aravind Sivaramakrishnan, Edgar Granados, Kostas E. Bekris

It also discusses how machine learning has been used to provide data-driven models of robots, which can then be used by a SBMP.

Computational Efficiency Motion Planning

Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

3 code implementations6 Oct 2021 Rui Wang, Yinglong Miao, Kostas E. Bekris

The new monotone solver is integrated with a global planner to solve non-monotone instances with high-quality solutions fast.

Motion Planning Object +1

Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses

no code implementations11 Aug 2020 Rui Wang, Chaitanya Mitash, Shiyang Lu, Daniel Boehm, Kostas E. Bekris

This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene.

6D Pose Estimation Object

Efficient Model Identification for Tensegrity Locomotion

no code implementations12 Apr 2018 Shaojun Zhu, David Surovik, Kostas E. Bekris, Abdeslam Boularias

This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks.

Bayesian Optimization

Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search

no code implementations24 Oct 2017 Chaitanya Mitash, Abdeslam Boularias, Kostas E. Bekris

Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.

6D Pose Estimation 6D Pose Estimation using RGB +4

Fast Model Identification via Physics Engines for Data-Efficient Policy Search

no code implementations24 Oct 2017 Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias

This paper presents a method for identifying mechanical parameters of robots or objects, such as their mass and friction coefficients.

Bayesian Optimization Friction +1

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

1 code implementation9 Mar 2017 Chaitanya Mitash, Kostas E. Bekris, Abdeslam Boularias

The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset.

Object object-detection +4

Analysis and Observations from the First Amazon Picking Challenge

no code implementations21 Jan 2016 Nikolaus Correll, Kostas E. Bekris, Dmitry Berenson, Oliver Brock, Albert Causo, Kris Hauser, Kei Okada, Alberto Rodriguez, Joseph M. Romano, Peter R. Wurman

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.

Robotics

A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place

no code implementations3 Sep 2015 Colin Rennie, Rahul Shome, Kostas E. Bekris, Alberto F. de Souza

This paper provides a new rich data set for advancing the state-of-the-art in RGBD- based 3D object pose estimation, which is focused on the challenges that arise when solving warehouse pick- and-place tasks.

Object object-detection +2

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