Reachability-based signed Distance Function

Reachability-based Trajectory Design with Neural Implicit Safety Constraints

RSS 2023

Jonathan Michaux

Qingyi Chen

Yongseok Kwon

Ram Vasudevan





All authors affiliated with the Robotics Institute, deparment of Computer Science and Engineering, and department of Mechanical Engineering of the University of Michigan, Ann Arbor.


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 strict safety requirements to avoid damaging itself or harming nearby humans. This is particularly challenging if the robot must also operate in real-time to quickly adjust to changes in its environment.
This paper addresses these challenges by proposing Reachability-based Signed Distance Functions (RDFs) as a neural implicit representation for robot safety. RDF, trained using supervised learning, accurately predicts the distance between the swept volume of a robot arm and an obstacle. RDF's inference and gradient computations are fast and scale linearly with the dimension of the system; these features enables its use within a novel real-time trajectory planning framework as a continuous- time collision-avoidance constraint. The planning method here is compared to state-of-the-art methods and is demonstrated to successfully solve challenging motion planning tasks for high- dimensional systems under a limited planning time horizon.


Example Scenario


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