4 citations · 5 across the 8 of their papers we have counts for
8 papers
Conformalized Reachable Sets for Obstacle Avoidance With Spheres
Yongseok Kwon, Jonathan Michaux, Seth Isaacson +4
Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments. Motion plans must be safe to ensure that the robot does not harm humans…
Let's Make a Splan: Risk-Aware Trajectory Optimization in a Normalized Gaussian Splat
Jonathan Michaux, Seth Isaacson, Challen Enninful Adu +6
Neural Radiance Fields and Gaussian Splatting have recently transformed computer vision by enabling photo-realistic representations of complex scenes. However, they have seen limit…
Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
Jonathan Michaux, Qingyi Chen, Challen Enninful Adu +2
Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging. This is because a future trajectory need…
Safe Planning for Articulated Robots Using Reachability-based Obstacle Avoidance With Spheres
Jonathan Michaux, Adam Li, Qingyi Chen +3
Generating safe motion plans in real-time is necessary for the wide-scale deployment of robots in unstructured and human-centric environments. These motion plans must be safe to en…
Serving Time: Real-Time, Safe Motion Planning and Control for Manipulation of Unsecured Objects
Zachary Brei, Jonathan Michaux, Bohao Zhang +2
A key challenge to ensuring the rapid transition of robotic systems from the industrial sector to more ubiquitous applications is the development of algorithms that can guarantee s…
Reachability-based Trajectory Design with Neural Implicit Safety Constraints
Jonathan Michaux, Qingyi Chen, Yongseok Kwon +1
Generating safe motion plans in real-time is a key requirement for deploying robot manipulators to assist humans in collaborative settings. In particular, robots must satisfy stric…