16 citations · 57 across the 46 of their papers we have counts for
7 papers · 1 filter
MBot: A Modular Ecosystem for Scalable Robotics Education
Peter Gaskell, Jana Pavlasek, Tom Gao +3
The Michigan Robotics MBot is a low-cost mobile robot platform that has been used to train over 1,400 students in autonomous navigation since 2014 at the University of Michigan and…
Stein Variational Belief Propagation for Multi-Robot Coordination
Jana Pavlasek, Joshua Jing Zhi Mah, Ruihan Xu +2
Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of ob…
The Michigan Robotics Undergraduate Curriculum: Defining the Discipline of Robotics for Equity and Excellence
Odest Chadwicke Jenkins, Jessy Grizzle, Ella Atkins +6
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…
TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation
Huijie Zhang, Anthony Opipari, Xiaotong Chen +3
Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detec…
Counter-Hypothetical Particle Filters for Single Object Pose Tracking
Elizabeth A. Olson, Jana Pavlasek, Jasmine A. Berry +1
Particle filtering is a common technique for six degrees of freedom (6D) pose estimation due to its ability to tractably represent belief over object pose. However, the particle fi…
SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions
Cameron Kisailus, Daksh Narang, Matthew Shannon +1
Recent advances in robotic mobile manipulation have spurred the expansion of the operating environment for robots from constrained workspaces to large-scale, human environments. In…
DNBP: Differentiable Nonparametric Belief Propagation
Anthony Opipari, Jana Pavlasek, Chao Chen +3
We present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm. Existing nonparametric belief propagatio…