48 citations · 136 across the 23 of their papers we have counts for
24 papers
RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments
Adam Polevoy, Dillon Capalongo, Katherine Tang +3
In this paper, we present an approach for combining stochastic nonlinear model predictive control (SNMPC) and reinforcement learning (RL) to enable probabilistically-safe perceptio…
Multi-Agent Feedback Motion Planning using Probably Approximately Correct Nonlinear Model Predictive Control
Mark Gonzales, Adam Polevoy, Marin Kobilarov +1
For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major c…
A Feasible Workflow for Retinal Vein Cannulation in Ex Vivo Porcine Eyes with Robotic Assistance
Peiyao Zhang, Peter Gehlbach, Marin Kobilarov +1
A potential Retinal Vein Occlusion (RVO) treatment involves Retinal Vein Cannulation (RVC), which requires the surgeon to insert a microneedle into the affected retinal vein and ad…
Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks
Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall +4
We explore whether surgical manipulation tasks can be learned on the da Vinci robot via imitation learning. However, the da Vinci system presents unique challenges which hinder str…
Uncertainty-Aware Planning for Heterogeneous Robot Teams using Dynamic Topological Graphs and Mixed-Integer Programming
Cora A. Duggan, Kevin C. Wolfe, Bradley Woosley +2
Multi-robot planning and coordination in uncertain environments is a fundamental computational challenge, since the belief space increases exponentially with the number of robots.…
Robust Perception-Based Navigation using PAC-NMPC with a Learned Value Function
Adam Polevoy, Mark Gonzales, Marin Kobilarov +1
Nonlinear model predictive control (NMPC) is typically restricted to short, finite horizons to limit the computational burden of online optimization. As a result, global planning f…
A Small Form Factor Aerial Research Vehicle for Pick-and-Place Tasks with Onboard Real-Time Object Detection and Visual Odometry
Cora A. Dimmig, Anna Goodridge, Gabriel Baraban +3
This paper introduces a novel, small form-factor, aerial vehicle research platform for agile object detection, classification, tracking, and interaction tasks. General-purpose hard…
Towards Deep Learning Guided Autonomous Eye Surgery Using Microscope and iOCT Images
Ji Woong Kim, Shuwen Wei, Peiyao Zhang +4
Recent advancements in retinal surgery have paved the way for a modern operating room equipped with a surgical robot, a microscope, and intraoperative optical coherence tomography…
Micromanipulation in Surgery: Autonomous Needle Insertion Inside the Eye for Targeted Drug Delivery
Ji Woong Kim, Peiyao Zhang, Peter Gehlbach +2
We consider a micromanipulation problem in eye surgery, specifically retinal vein cannulation (RVC). RVC involves inserting a microneedle into a retinal vein for the purpose of tar…
Deep Learning Guided Autonomous Surgery: Guiding Small Needles into Sub-Millimeter Scale Blood Vessels
Ji Woong Kim, Peiyao Zhang, Peter Gehlbach +2
We propose a general strategy for autonomous guidance and insertion of a needle into a retinal blood vessel. The main challenges underpinning this task are the accurate placement o…
Autonomous Needle Navigation in Retinal Microsurgery: Evaluation in ex vivo Porcine Eyes
Peiyao Zhang, Ji Woong Kim, Peter Gehlbach +2
Important challenges in retinal microsurgery include prolonged operating time, inadequate force feedback, and poor depth perception due to a constrained top-down view of the surger…
Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)
Adam Polevoy, Marin Kobilarov, Joseph Moore
Approaches for stochastic nonlinear model predictive control (SNMPC) typically make restrictive assumptions about the system dynamics and rely on approximations to characterize the…
Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance
James Guthrie, Marin Kobilarov, Enrique Mallada
Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation…
Learn Proportional Derivative Controllable Latent Space from Pixels
Weiyao Wang, Marin Kobilarov, Gregory D. Hager
Recent advances in latent space dynamics model from pixels show promising progress in vision-based model predictive control (MPC). However, executing MPC in real time can be challe…
Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration
Ji Woong Kim, Changyan He, Muller Urias +4
A fundamental challenge in retinal surgery is safely navigating a surgical tool to a desired goal position on the retinal surface while avoiding damage to surrounding tissues, a pr…
Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with Optimal Control
Ji Woong Kim, Peiyao Zhang, Peter Gehlbach +2
During retinal microsurgery, precise manipulation of the delicate retinal tissue is required for positive surgical outcome. However, accurate manipulation and navigation of surgica…
Sclera Force Control in Robot-assisted Eye Surgery: Adaptive Force Control vs. Auditory Feedback
Ali Ebrahimi, Changyan He, Niravkumar Patel +3
Surgeon hand tremor limits human capability during microsurgical procedures such as those that treat the eye. In contrast, elimination of hand tremor through the introduction of mi…
Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments
Chris Paxton, Vasumathi Raman, Gregory D. Hager +1
We consider task and motion planning in complex dynamic environments for problems expressed in terms of a set of Linear Temporal Logic (LTL) constraints, and a reward function. We…
Do What I Want, Not What I Did: Imitation of Skills by Planning Sequences of Actions
Chris Paxton, Felix Jonathan, Marin Kobilarov +1
We propose a learning-from-demonstration approach for grounding actions from expert data and an algorithm for using these actions to perform a task in new environments. Our approac…
Towards Robot Task Planning From Probabilistic Models of Human Skills
Chris Paxton, Marin Kobilarov, Gregory D. Hager
We describe an algorithm for motion planning based on expert demonstrations of a skill. In order to teach robots to perform complex object manipulation tasks that can generalize ro…
Multisymplectic Lie group variational integrator for a geometrically exact beam in R3
François Demoures, François Gay-Balmaz, Marin Kobilarov +1
In this paper we develop, study, and test a Lie group multisymplectic integra- tor for geometrically exact beams based on the covariant Lagrangian formulation. We exploit the multi…
Discrete Variational Optimal Control
Fernando Jimenez, Marin Kobilarov, David Martin de Diego
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of op…
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Andrzej Banaszuk, Vladimir A. Fonoberov, Thomas A. Frewen +8
Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving…
Simulating Nonholonomic Dynamics
M. Kobilarov, D. Martín de Diego, S. Ferraro
This paper develops different discretization schemes for nonholonomic mechanical systems through a discrete geometric approach. The proposed methods are designed to account for the…