Craig Knuth
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View article: Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability
Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability Open
For navigation of robots, image segmentation is an important component to determining a terrain's traversability. For safe and efficient navigation, it is key to assess the uncertainty of the predicted segments. Current uncertainty estimat…
View article: Rapid Co-design of Task-Specialized Whegged Robots for Ad-Hoc Needs
Rapid Co-design of Task-Specialized Whegged Robots for Ad-Hoc Needs Open
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 and wheg trajectory for a MiniRHex robot, producing 3D print…
View article: Generative Planning with Fast Collision Checks for High Speed Navigation
Generative Planning with Fast Collision Checks for High Speed Navigation Open
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 perceptual information. Often, this is tackled by methods that it…
View article: Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems
Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems Open
We present a method for providing statistical guarantees on runtime safety and goal reachability for integrated planning and control of a class of systems with unknown nonlinear stochastic underactuated dynamics. Specifically, given a dyna…
View article: Correction to “Planning With Learned Dynamics: Probabilistic Guarantees on Safety and Reachability Via Lipschitz Constants” [Jul21 5129-5136]
Correction to “Planning With Learned Dynamics: Probabilistic Guarantees on Safety and Reachability Via Lipschitz Constants” [Jul21 5129-5136] Open
We wish to make the following corrections and clarifications to our manuscript [1]. For a version of the manuscript that has these changes integrated into the text, please see [2]. •In [1], the method is claimed to provide safety guarantee…
View article: Complex Terrain Navigation via Model Error Prediction
Complex Terrain Navigation via Model Error Prediction Open
Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target. In deformable, complex terrain, using geometric-based approaches can fail to find a path due to mischar…
View article: High-Speed Robot Navigation using Predicted Occupancy Maps
High-Speed Robot Navigation using Predicted Occupancy Maps Open
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 bottleneck associated with explicit mapping and the limited fiel…
View article: Planning With Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
Planning With Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants Open
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 domain in which a learned control-affine approximation of the …
View article: Inferring Obstacles and Path Validity from Visibility-Constrained Demonstrations
Inferring Obstacles and Path Validity from Visibility-Constrained Demonstrations Open
View article: Planning with Learned Dynamics: Guaranteed Safety and Reachability via Lipschitz Constants.
Planning with Learned Dynamics: Guaranteed Safety and Reachability via Lipschitz Constants. Open
We present an approach for feedback motion planning of systems with unknown
dynamics which provides guarantees on safety, reachability, and stability about
the goal. Given a learned control-affine approximation of the true dynamics, we
est…
View article: Planning with Learned Dynamics: Probabilistic Guarantees on Safety and\n Reachability via Lipschitz Constants
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and\n Reachability via Lipschitz Constants Open
We present a method for feedback motion planning of systems with unknown\ndynamics which provides probabilistic guarantees on safety, reachability, and\ngoal stability. To find a domain in which a learned control-affine\napproximation of t…