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Found 21 papers, 6 papers with code
The Michigan Robotics Undergraduate Curriculum: Defining the Discipline of Robotics for Equity and Excellence
The Robotics Major at the University of Michigan was successfully launched in the 2022-23 academic year as an innovative step forward to better serve students, our communities, and our society.
TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation
TransNet is evaluated in terms of pose estimation accuracy on a large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach.
Counter-Hypothetical Particle Filters for Single Object Pose Tracking
However, the particle filter is prone to particle deprivation due to the high-dimensional nature of 6D pose.
SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions
We posit the notion of semantic frames provides a compelling representation for robot actions that is amenable to action-focused perception, task-level reasoning, action-level execution, and integration with language.
NARF22: Neural Articulated Radiance Fields for Configuration-Aware Rendering
We show the applicability of the model to gradient-based inference methods through a configuration estimation and 6 degree-of-freedom pose refinement task.
TransNet: Category-Level Transparent Object Pose Estimation
TransNet is evaluated in terms of pose estimation accuracy on a recent, large-scale transparent object dataset and compared to a state-of-the-art category-level pose estimation approach.
VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation
We hope the new simulator and benchmark will facilitate future research on language-guided robotic manipulation.
ClearPose: Large-scale Transparent Object Dataset and Benchmark
Transparent objects are ubiquitous in household settings and pose distinct challenges for visual sensing and perception systems.
ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception
We demonstrate the effectiveness of ProgressLabeller by rapidly create a dataset of over 1M samples with which we fine-tune a state-of-the-art pose estimation network in order to markedly improve the downstream robotic grasp success rates.
SeanNet: Semantic Understanding Network for Localization Under Object Dynamics
This paper proposes a SEmantic understANding Network (SeanNet) architecture that enables an effective learning process with coupled visual and semantic inputs.
Topologically-Informed Atlas Learning
We present a new technique that enables manifold learning to accurately embed data manifolds that contain holes, without discarding any topological information.
Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable
In the most recent wave research in AI has largely focused on deep (i. e., many-layered) neural networks, which are loosely inspired by the brain and trained by "deep learning" methods.
Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames
In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF).
Parts-Based Articulated Object Localization in Clutter Using Belief Propagation
To address this problem, we present a generative-discriminative parts-based recognition and localization method for articulated objects in clutter.
Semantic Linking Maps for Active Visual Object Search
SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations.
LIT: Light-field Inference of Transparency for Refractive Object Localization
With respect to this ProLIT dataset, we demonstrate that LIT can outperform both state-of-the-art end-to-end pose estimation methods and a generative pose estimator on transparent objects.
GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter
We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing.
GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation.
Semantic Mapping with Simultaneous Object Detection and Localization
Our results demonstrate that the particle filtering based inference of CT-Map provides improved object detection and pose estimation with respect to baseline methods that treat observations as independent samples of a scene.
Robotics
Pull Message Passing for Nonparametric Belief Propagation
In contrast, we propose a "pull" method, as the Pull Message Passing for Nonparametric Belief propagation (PMPNBP) algorithm, and demonstrate its viability for efficient inference.
Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency
In order to fully function in human environments, robot perception will need to account for the uncertainty caused by translucent materials.
