High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning (2310.03624v2)

Abstract: A robot self-model is a task-agnostic representation of the robot's physical morphology that can be used for motion planning tasks in the absence of a classical geometric kinematic model. In particular, when the latter is hard to engineer or the robot's kinematics change unexpectedly, human-free self-modeling is a necessary feature of truly autonomous agents. In this work, we leverage neural fields to allow a robot to self-model its kinematics as a neural-implicit query model learned only from 2D images annotated with camera poses and configurations. This enables significantly greater applicability than existing approaches which have been dependent on depth images or geometry knowledge. To this end, alongside a curricular data sampling strategy, we propose a new encoder-based neural density field architecture for dynamic object-centric scenes conditioned on high numbers of degrees of freedom (DOFs). In a 7-DOF robot test setup, the learned self-model achieves a Chamfer-L2 distance of 2% of the robot's workspace dimension. We demonstrate the capabilities of this model on motion planning tasks as an exemplary downstream application.

• The paper introduces an encoder-based neural density field architecture that models robot kinematics using 2D images with annotated camera poses.
• It employs differentiable rendering and separate encoders for spatial and DOF configurations to reconstruct accurate 3D representations of robot morphology.
• The approach achieves a 2% Chamfer-L2 distance on a 7-DOF robot testbed, demonstrating effective self-modeling for practical motion planning tasks.

High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning

This paper, authored by Lennart Schulze and Hod Lipson, presents a novel approach to robot self-modeling using high-degrees-of-freedom dynamic neural fields. The authors propose an encoder-based neural density field architecture capable of representing object-centric scenes with numerous degrees of freedom (DOFs), driven solely by 2D images annotated with camera poses and configurations. The methodology allows for the reconstruction of a robot's physical morphology in scenarios where classical geometric kinematic models may be infeasible.

The presented work builds upon neural fields combined with differentiable rendering, enabling the learning of 3D scene representations from multiple camera views. By eschewing the traditional reliance on depth images or explicit geometry information, the authors introduce a neural-implicit query model capable of self-modeling robot kinematics in a substantially more versatile manner than existing approaches. The design incorporates curricular data sampling and a specialized MLP architecture that leverages separate encoders for spatial coordinates and DOF configurations to create a continuous map, yielding a comprehensive kinematic representation.

The authors validate their approach on a 7-DOF robot testbed, achieving an average Chamfer-L2 distance of 2% relative to the robot's workspace dimension, a promising result indicating close spatial proximity between predicted and ground-truth models. Furthermore, practical application is demonstrated through motion planning tasks, utilizing the learned model's forward and inverse kinematics for trajectory generation.

Schulze and Lipson's work draws significant implications for future developments in AI, particularly in robotics where self-modeling is crucial for autonomous agents. By circumventing depth and geometric model requirements, this work potentially paves the way for rapid adoption in dynamic environments, enhancing resilience in robots subjected to unforeseen morphological changes. The introduction of high-DOFs neural fields also promises advancements in related fields, such as automotive or aeronautics, where complex dynamic modeling is required.

From a theoretical perspective, the flexibility of dynamic neural fields in representing both spatial and parametric changes suggests broader applications beyond robotics. Future developments might explore the integration with environment modeling, handling multi-agent systems, and applications in dynamically controlled environments across various domains. The potential to extend this approach by integrating it with existing neural field methodologies representing environmental features could generate richer, joint models, enhancing decision-making processes and task execution in robotics and other fields requiring nuanced representation of intricate systems.