Search Results for author: Bowen Wen

Found 22 papers, 13 papers with code

Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

1 code implementation1 Apr 2024 Yijia Weng, Bowen Wen, Jonathan Tremblay, Valts Blukis, Dieter Fox, Leonidas Guibas, Stan Birchfield

We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states.


FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

1 code implementation13 Dec 2023 Bowen Wen, Wei Yang, Jan Kautz, Stan Birchfield

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups.

3D Object Detection 3D Object Tracking +7

MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

no code implementations26 Oct 2023 Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, Dieter Fox

Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.

Imitation Learning

Diff-DOPE: Differentiable Deep Object Pose Estimation

no code implementations30 Sep 2023 Jonathan Tremblay, Bowen Wen, Valts Blukis, Balakumar Sundaralingam, Stephen Tyree, Stan Birchfield

We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a 3D textured model of an object, and an initial pose of the object.

Object Pose Estimation +1

TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation

no code implementations CVPR 2023 Taeyeop Lee, Jonathan Tremblay, Valts Blukis, Bowen Wen, Byeong-Uk Lee, Inkyu Shin, Stan Birchfield, In So Kweon, Kuk-Jin Yoon

Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.

Object Pose Estimation +2

BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

1 code implementation CVPR 2023 Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Muller, Alex Evans, Dieter Fox, Jan Kautz, Stan Birchfield

We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object.

3D Object Tracking 3D Reconstruction +5

One-Shot Neural Fields for 3D Object Understanding

no code implementations21 Oct 2022 Valts Blukis, Taeyeop Lee, Jonathan Tremblay, Bowen Wen, In So Kweon, Kuk-Jin Yoon, Dieter Fox, Stan Birchfield

At test-time, we build the representation from a single RGB input image observing the scene from only one viewpoint.

3D Reconstruction Decoder +3

Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation

1 code implementation18 Oct 2022 Yunzhi Lin, Thomas Müller, Jonathan Tremblay, Bowen Wen, Stephen Tyree, Alex Evans, Patricio A. Vela, Stan Birchfield

We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene.

Pose Estimation

Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations

no code implementations8 Mar 2022 Junchi Liang, Bowen Wen, Kostas Bekris, Abdeslam Boularias

This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person.

Imitation Learning Object +1

You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

2 code implementations30 Jan 2022 Bowen Wen, Wenzhao Lian, Kostas Bekris, Stefan Schaal

The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.

3D Object Tracking Industrial Robots +6

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

1 code implementation19 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.

Domain Generalization Grasp Contact Prediction +6

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.

 Ranked #1 on 6D Pose Estimation using RGBD on REAL275 (mAP 3DIou@25 metric)

3D Object Tracking 6D Pose Estimation +7

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 +2

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 Robot Manipulation

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.


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

Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment

1 code implementation journal 2018 Bowen Wen, Sukru Yaren Gelbal, Bilin Aksun Guvenc, Levent Guvenc

The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles.

Autonomous Driving Decision Making +2

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