A Differentiable Physics Engine for Deep Learning in Robotics (1611.01652v2)

Abstract: An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used, models are kept small or rely on finite difference approximations for the Jacobian. This method quickly grows expensive with increasing numbers of parameters, such as found in deep learning. We propose the implementation of a modern physics engine, which can differentiate control parameters. This engine is implemented for both CPU and GPU. Firstly, this paper shows how such an engine speeds up the optimization process, even for small problems. Furthermore, it explains why this is an alternative approach to deep Q-learning, for using deep learning in robotics. Finally, we argue that this is a big step for deep learning in robotics, as it opens up new possibilities to optimize robots, both in hardware and software.

• The paper introduces a differentiable physics engine that provides analytic gradients for robotic control, enabling gradient-based optimization over traditional derivative-free methods.
• The engine leverages Theano for 3D rigid body dynamics and demonstrates faster convergence in tasks like ball throwing and quadrupedal gait optimization.
• The paper validates the engine’s scalability on complex tasks, including optimizing a 17,284-parameter neural network for a robotic arm, highlighting its potential for deep learning in robotics.

An Examination of Differentiable Physics Engines in Robotics Control Optimization

The research paper, "A Differentiable Physics Engine for Deep Learning in Robotics," authored by Jonas Degrave, Michiel Hermans, Joni Dambre, and Francis Wyffels, introduces a notable advancement in the domain of robotics control optimization. The paper focuses on the development and evaluation of a physics engine capable of differentiating control parameters, which holds significance for enhancing the efficiency of controller optimization in robotic systems through gradient-based methods.

Core Contributions

The principal contribution of this paper lies in the implementation of a differentiable physics engine. Unlike conventional physics engines that treat the robot as a non-differentiable system necessitating derivative-free methods such as evolutionary algorithms, this engine facilitates the use of gradient-based optimization by providing analytical gradients. The research emphasizes the advantages of this approach in terms of computation speed and parameter optimization, exemplified by several experiments.

1. Architecture and Implementation: The differentiable physics engine is built from scratch for both CPU and GPU using Theano, catering to 3D rigid body dynamics. The engine leverages automatic differentiation to obtain analytic gradients, allowing optimization processes akin to training Recurrent Neural Networks (RNNs) using techniques such as Backpropagation Through Time (BPTT).
2. Performance Evaluation: Through experiments such as optimizing the initial conditions for a ball-throwing task and controlling a quadrupedal robot, the authors demonstrate that this method can surpass traditional derivative-free optimization in terms of convergence speed. Notably, gradient-based optimization proved superior even with only six parameters for the ball scenario.
3. Complex Tasks: The research further explores more challenging control tasks, such as the manipulation of a four-degree-of-freedom robotic arm and an inverted pendulum controlled via vision processing. These tasks validate the engine's capability to handle complex, high-dimensional neural network controllers.

Technical and Numerical Insights

The differentiable engine leads to remarkable reductions in optimization iterations. For instance, optimizing the robot arm to reach a fixed point with a neural network of 17,284 parameters required only 150 model evaluations to achieve fine precision, a feat unattainable by conventional methods like CMA-ES when dealing with such parameter complexity.

For the quadrupedal robot, the optimization of a hopping gait achieved a significant enhancement in speed, with the engine processing up to 864,000 model seconds per day using batch methods on GPU. This capacity underscores the scalability of the approach to optimize neural controllers with millions of parameters efficiently.

Implications and Future Directions

This research showcases the practical implications of differentiable physics engines in robotics. The adoption of gradient-based methods via such engines could revolutionize how control policies are optimized, potentially accelerating the integration of deep learning techniques in robotic applications. The differentiated model provides a conducive environment for more sophisticated controller designs, such as those incorporating Long Short-Term Memory (LSTM) networks.

Moreover, the potential to incorporate differentiable sensory devices like cameras signifies the broader applicability of the engine in tasks requiring real-time perception and control. Exploring adversarial training strategies with differentiable engines could further enrich robotic control approaches, similar to trends observed in GANs for visual data.

In conclusion, while the differentiable physics engine presents a transformative tool for robotic control, further exploration into transferring these models to physical robots, addressing issues like model discrepancies and sensor noise, would be critical for widespread adoption. Future research could also investigate the synergy between model-based and data-driven approaches in robotics, leveraging the advancements in computational capabilities provided by such engines.