Differentiable and Learnable Robot Models (2202.11217v1)

Abstract: Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics engines motivated by the computational benefits of merging rigid body simulations with modern differentiable machine learning libraries. Here, we present a library that focuses on the ability to combine data driven methods with analytical rigid body computations. More concretely, our library \emph{Differentiable Robot Models} implements both \emph{differentiable} and \emph{learnable} models of the kinematics and dynamics of robots in Pytorch. The source-code is available at \url{https://github.com/facebookresearch/differentiable-robot-model}

• The paper presents a novel PyTorch library that integrates analytical robot modeling with learnable dynamics to advance robotics research.
• The paper leverages parsed URDFs to construct computation graphs, enabling GPU-accelerated simulations of kinematics and dynamics.
• The paper demonstrates applications in model-based reinforcement learning and parameter identification while also serving as a versatile educational tool.

Differentiable and Learnable Robot Models: An Overview

The paper "Differentiable and Learnable Robot Models" presents a specialized library designed to enhance the intersection of robotics and differentiable programming, implemented within the PyTorch framework. This library innovatively focuses on bridging the gap between analytical computations of robot kinematics and dynamics with the flexibility and data-driven insight offered by learnable models.

Key Contributions

The authors develop a library that stands apart from similar contemporary software by emphasizing differentiable robot models rather than offering full simulation environments. By implementing both kinematic and dynamic representations of robots in a fully differentiable manner, the library enables the integration of these representations seamlessly into modern deep learning workflows.

1. Framework and Structure: The library constructs analytical models using parsed URDFs to create computation graphs, offering a robust foundation for developing simulations that are both differentiable and learnable. This feature allows for adaptation across diverse robotic configurations characterized by kinematic trees.
2. Optimization and Learning Adaptability: The library can serve multiple use cases, from acting as a ground truth model in model-based reinforcement learning (MBRL) to facilitating the identification of unknown parameters in robotics research. Via automatic differentiation, it provides a pathway to integrate and optimize neural structures within robotics frameworks, potentially advancing the search for highly optimized networks analogous to CNNs in vision tasks.
3. Teaching and Research Tool: By introducing a contemporary deep learning approach to understanding robotics, the library also presents itself as an invaluable educational tool. It allows educators to demonstrate core robotics concepts within a framework familiar to students engaged with neural networks and deep learning architecture.

Library Architecture

The library represents an implementation using spatial vector notation, providing comprehensive coverage of robot kinematics through operations like forward kinematics and Jacobian computations. For dynamics, it includes robust implementations such as the Recursive Newton Euler Algorithm, the Composite Rigid Body Algorithm, and the Articulated Body Algorithm. Each of these algorithms is implemented to leverage GPU parallel processing, aligning with the computational needs of modern robotics research.

Practical Implications

From a practical standpoint, this library lowers the barrier to incorporating advanced machine learning techniques into the modeling and simulation of robotic systems. It enables a highly modular approach where different components of a robot's dynamics or kinematics can be made learnable, permitting nuanced exploration into data-driven parameter tuning and adaptation.

Theoretical Implications and Future Directions

Beyond practical applications, the library's structure posits theoretical considerations regarding the hybrid modeling of robotic systems, wherein both physics-based and data-driven models are considered. The ability to differentiate through these models provides new opportunities for optimizing performance and control strategies, pertinent topics in advanced robotics research.

The research sets the foundation for subsequent explorations into how differentiable and learnable models can be applied to more sophisticated robotic systems, potentially unifying the fields of robotics and machine learning further. Future work could extend support to more complex robotic systems and explore integrating more sophisticated neural network structures that adapt dynamically to real-world data.

In conclusion, the presented library marks a significant contribution to the ongoing efforts to seamlessly integrate differentiable programming paradigms with robotics research. Its adaptability, rooted in leveraging PyTorch's powerful deep learning scaffolding, positions it as a strong catalyst for both theoretical research and practical applications in robotics.