Search Results for author: Kostas Bekris

Found 14 papers, 4 papers with code

CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

no code implementations19 Sep 2021 Bowen Wen, Wenzhao Lian, Kostas Bekris, Stefan Schaal

This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation.

BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models

1 code implementation1 Aug 2021 Bowen Wen, Kostas Bekris

Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching.

3D Object Tracking 6D Pose Estimation +6

Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks

1 code implementation26 Jun 2021 Andrew S. Morgan, Bowen Wen, Junchi Liang, Abdeslam Boularias, Aaron M. Dollar, Kostas Bekris

Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems.

Motion Planning Object Tracking

Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains

1 code implementation29 May 2021 Bowen Wen, Chaitanya Mitash, Kostas Bekris

This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking.

Pose Tracking

Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop

no code implementations7 Dec 2020 Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, Melissa Mozifian, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White

This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.

Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots

no code implementations10 Nov 2020 Kun Wang, Mridul Aanjaneya, Kostas Bekris

The results indicate that only 0. 25\% of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is used for training against training the policy directly on the ground truth system.

Spring-Rod System Identification via Differentiable Physics Engine

no code implementations9 Nov 2020 Kun Wang, Mridul Aanjaneya, Kostas Bekris

We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies.

Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects

no code implementations28 Jun 2020 Chaitanya Mitash, Rahul Shome, Bowen Wen, Abdeslam Boularias, Kostas Bekris

The effectiveness of the proposed approach is demonstrated by developing a robotic system that picks a previously unseen object from a table-top and places it in a constrained space.

Robotics

A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines

no code implementations28 Apr 2020 Kun Wang, Mridul Aanjaneya, Kostas Bekris

We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies.

That and There: Judging the Intent of Pointing Actions with Robotic Arms

1 code implementation13 Dec 2019 Malihe Alikhani, Baber Khalid, Rahul Shome, Chaitanya Mitash, Kostas Bekris, Matthew Stone

This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature.

Common Sense Reasoning

Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects

no code implementations11 Oct 2019 Chaitanya Mitash, Bowen Wen, Kostas Bekris, Abdeslam Boularias

To evaluate this method, a dataset of densely packed objects with challenging setups for state-of-the-art approaches is collected.

6D Pose Estimation

Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter

no code implementations25 Jun 2018 Chaitanya Mitash, Abdeslam Boularias, Kostas Bekris

This work proposes an autonomous process for pose estimation that spans from data generation to scene-level reasoning and self-learning.

Object Detection Object Recognition +1

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