Regularized Deep Signed Distance Fields for Reactive Motion Generation
This work addresses the need for efficient distance field computation in robotics to enhance safety and collaboration in dynamic settings, though it appears incremental as it builds on existing neural implicit methods.
The paper tackles the problem of real-time collision avoidance for robots in dynamic environments by proposing Regularized Deep Signed Distance Fields (ReDSDF), a neural implicit function that computes smooth distance fields at any scale, enabling safe human-robot interaction and whole-body control in simulated tasks and a real-world handover application.
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting both humans and their hardware. However, different applications require different distance resolutions, leading to various heuristic approaches for measuring distance fields w.r.t. obstacles, which are computationally expensive and hinder their application in dynamic obstacle avoidance use-cases. We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale, with fine-grained resolution over high-dimensional manifolds and articulated bodies like humans, thanks to our effective data generation and a simple inductive bias during training. We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces. Finally, we provide proof of concept of a real-world application in a HRI handover task with a mobile manipulator robot.