ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (1810.01054v1)

Abstract: Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.

• The paper introduces a real-time differentiable simulator leveraging MLS-MPM for precise gradient computation in soft robotics.
• It utilizes GPU acceleration to simulate 64,000 particles at over 628 frames per second, outperforming alternatives like NVIDIA Flex.
• The open-source framework facilitates optimal control and co-design, reducing barriers for advanced research in soft robotics.

Overview of ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

The paper details the development of ChainQueen, a real-time differentiable simulator aimed at advancing the simulation efficacy of deformable objects, particularly within the domain of soft robotics. Utilizing the Moving Least Squares Material Point Method (MLS-MPM), ChainQueen provides a solution to the computational challenges intrinsic to the simulation of deformable objects, which include high degrees of freedom and complex contact dynamics.

Key Contributions and Methodology

ChainQueen leverages the MLS-MPM, a hybrid Lagrangian-Eulerian method, to simulate deformable objects efficiently. What distinguishes this simulator is its differentiability with respect to both state and model parameters, facilitating seamless incorporation into gradient-based optimization frameworks. This capability is particularly valuable for inverse problem applications, such as optimal control and motion planning, where precise gradient calculation greatly enhances performance.

One of the primary challenges addressed by this work is the slow and computationally intensive process of simulating deformable objects due to their complex motion and collision dynamics. ChainQueen addresses these issues by integrating an actuation model that supports a variety of actuation types, making it well-suited for practical applications in soft robotics.

The implementation leverages GPU acceleration, achieving significant computational speed, with performance benchmarks indicating that ChainQueen outpaces existing alternatives like NVIDIA Flex. Benchmarks demonstrate ChainQueen's ability to simulate 64,000 particles in three-dimensional space at rates upwards of 628 frames per second.

Results and Impact

The paper presents a series of evaluations, demonstrating both the accuracy and performance efficiency of ChainQueen. Simulation tasks, ranging from controlling soft robotics systems to co-design problems, showcase the simulator's capabilities. Noteworthy outcomes include:

• Efficient convergence in optimization tasks, such as control and co-design, facilitated by the precise gradient information from the simulator.
• High simulation fidelity as evidenced by both forward simulations and the accurate computation of gradients.
• Practical open-source implications, with a user-friendly interface that caters to a wide audience, lowering the barrier to developing custom soft robotics systems.

The distinguishing numerical results include a relative gradient error as low as $9.80 \times 10^{-8}$ in specific test cases, underscoring ChainQueen's numerical precision.

Implications for the Future

ChainQueen's differentiability opens up several avenues for advancing research in robotics and beyond. Its utility in gradient-based optimization paves the way for more sophisticated control strategies in robotic systems. Additionally, the ability to infer physical properties through simulation suggests potential applications in system identification and adaptive robotics.

Future research could explore the integration of ChainQueen with rigid body simulators, potentially expanding the scope of its applicability to hybrid systems that incorporate both deformable and rigid components. Moreover, the open-sourcing of ChainQueen's codebase is likely to spur further innovation and collaboration across the robotics research community.

In conclusion, ChainQueen represents a significant contribution to the toolkit available for the paper and development of soft robotics systems, providing a scalable and efficient platform for simulation enhanced by its focus on real-time differentiability.