Regularized Deep Signed Distance Fields for Reactive Motion Generation

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.

Authors

• Puze Liu
• Kuo Zhang
• Davide Tateo
• Snehal Jauhri
• Jan Peters 0001
• Georgia Chalvatzaki

Keywords

• Training
• Human-robot interaction
• Hardware
• Planning
• Safety
• Reliability
• Task analysis
• Distance Map
• Signed Distance Function
• Reactive Motion
• Signed Distance
• Collision
• Heuristic
• Implicit Function
• Robot Manipulator
• Obstacle Avoidance
• Simulated Task
• Robot Interaction
• Inductive Bias
• Smooth Field
• Coworking Spaces
• Mobile Manipulator
• State Space
• Motor Control
• Distance Function
• Points In Space
• Robot Control
• Point Cloud
• Human Pose
• Artificial Potential Field
• Loss Of Components
• Reactive Control
• Query Point
• Path Planning
• Data Augmentation
• Approaches In The Literature