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Found 22 papers, 13 papers with code
Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
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
We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups.
MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.
Diff-DOPE: Differentiable Deep Object Pose Estimation
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.
HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions
We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks.
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
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.
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
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.
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world.
One-Shot Neural Fields for 3D Object Understanding
At test-time, we build the representation from a single RGB input image observing the scene from only one viewpoint.
Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation
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.
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations
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.
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
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
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)
Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks
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.
Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking.
se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Tracking the 6D pose of objects in video sequences is important for robot manipulation.
Ranked #5 on
6D Pose Estimation
on YCB-Video
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects
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
Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands
Precise in-hand manipulation is an important skill for a robot to perform tasks in human environments.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
The hand's point cloud is pruned and robust global registration is performed to generate object pose hypotheses, which are clustered.
6D Pose Estimation using RGB
6D Pose Estimation using RGBD
+4
Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects
To evaluate this method, a dataset of densely packed objects with challenging setups for state-of-the-art approaches is collected.
Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment
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.
