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cs.RO2024

Rapid Co-design of Task-Specialized Whegged Robots for Ad-Hoc Needs

Varun Madabushi, Katie M. Popek, Craig Knuth +2

In this work, we investigate the use of co-design methods to iterate upon robot designs in the field, performing time sensitive, ad-hoc tasks. Our method optimizes the morphology a…

cs.RO2024

Generative Planning with Fast Collision Checks for High Speed Navigation

Craig Knuth, Cora Dimmig, Brian Bittner

Reasoning about large numbers of diverse plans to achieve high speed navigation in cluttered environments remains a challenge for robotic systems even in the case of perfect percep…

cs.RO2020

High-Speed Robot Navigation using Predicted Occupancy Maps

Kapil D. Katyal, Adam Polevoy, Joseph Moore +2

Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottl…

cs.RO2020

Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants

Craig Knuth, Glen Chou, Necmiye Ozay +1

We present a method for feedback motion planning of systems with unknown dynamics which provides probabilistic guarantees on safety, reachability, and goal stability. To find a dom…

cs.RO2020

Inferring Obstacles and Path Validity from Visibility-Constrained Demonstrations

Craig Knuth, Glen Chou, Necmiye Ozay +1

Many methods in learning from demonstration assume that the demonstrator has knowledge of the full environment. However, in many scenarios, a demonstrator only sees part of the env…