DiffMimic: Efficient Motion Mimicking with Differentiable Physics (2304.03274v2)

Abstract: Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or even days of training to mimic a simple motion sequence, resulting in poor scalability. In this work, we leverage differentiable physics simulators (DPS) and propose an efficient motion mimicking method dubbed DiffMimic. Our key insight is that DPS casts a complex policy learning task to a much simpler state matching problem. In particular, DPS learns a stable policy by analytical gradients with ground-truth physical priors hence leading to significantly faster and stabler convergence than RL-based methods. Moreover, to escape from local optima, we utilize a Demonstration Replay mechanism to enable stable gradient backpropagation in a long horizon. Extensive experiments on standard benchmarks show that DiffMimic has a better sample efficiency and time efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a physically simulated character to learn Backflip after 10 minutes of training and be able to cycle it after 3 hours of training, while the existing approach may require about a day of training to cycle Backflip. More importantly, we hope DiffMimic can benefit more differentiable animation systems with techniques like differentiable clothes simulation in future research.

• The paper introduces DiffMimic, which reformulates motion mimicking into a state matching problem using differentiable physics for enhanced sample efficiency.
• It leverages analytical optimization and demonstration replay to bypass complex reward engineering and stabilize gradient flow.
• Empirical results show lower pose errors and faster training times compared to RL benchmarks, paving the way for real-time physics-based simulations.

DiffMimic: Efficient Motion Mimicking with Differentiable Physics

The paper "DiffMimic: Efficient Motion Mimicking with Differentiable Physics" addresses the challenges associated with motion mimicking in physics-based character animation, a domain traditionally dominated by reinforcement learning (RL) techniques. The authors propose an alternative solution that leverages differentiable physics simulators (DPS) to create a more efficient and effective motion mimicking method, termed DiffMimic. This approach focuses on reformulating the motion mimicking task into a state matching problem, which results in improved sample efficiency and convergence speed compared to established RL-based methodologies.

Main Contributions

Differentiable Physics Simulators (DPS)

DPS are a central component in DiffMimic's methodology. They enable the calculation of analytical gradients directly from physical simulations, thereby transforming the motion mimicking task from learning complex policies to aligning states with ground truth trajectories. This transition allows for a more direct optimization process, enhancing both speed and stability in learning compared to traditional RL methods. The authors leverage these analytical gradients to bypass the need for elaborate reward engineering, a common bottleneck in RL-based techniques.

Analytical Optimization and Demonstration Replay

The paper highlights two significant optimizations within DiffMimic:

1. Analytical Optimization: By using DPS, the optimization problem transitions from a conventional RL framework involving policy gradients to a simpler analytical gradient approach. This methodological shift significantly boosts sample and time efficiency. For instance, the system can teach a simulated character to perform a backflip within 10 minutes and maintain it after 3 hours of training, a process that typically requires a day in standard RL approaches.
2. Demonstration Replay: To overcome the pitfalls of local minima and gradient vanishing/exploding—common issues in gradient-based methods—DiffMimic incorporates a Demonstration Replay mechanism. This approach strategically replaces certain states in a policy rollout with states from a demonstration trajectory to stabilize learning and ensure smooth gradient flow.

Results and Implications

The empirical evaluations underscore DiffMimic's superiority in both sample and time efficiency over RL benchmarks such as DeepMimic. By recording lower pose errors across various complex tasks, the proposed method not only demonstrates high robustness but also significantly decreases the computation resources required. These improvements suggest that DiffMimic is not only a viable alternative to RL-based methods but also offers a scalable solution for real-time and computationally constrained applications.

Future Directions and Applications

The implications of this research extend beyond simple task efficiency. DiffMimic opens avenues for integrating differentiable components in more intricate animation systems, potentially impacting fields such as differentiable cloth simulation. Additionally, the method's ability to handle complex dynamics with comparative ease marks a step towards universal applicability across various physics-based simulations and robot control tasks.

Moreover, the inherent scalability of the DPS framework used in DiffMimic holds promise for broader adoption in domains necessitating rapid prototyping or real-time feedback systems. As the approach does not rely heavily on reward function customization, it offers a template for straightforward adaptation to diverse and novel scenarios.

Conclusion

In summary, "DiffMimic: Efficient Motion Mimicking with Differentiable Physics" presents a compelling case for transitioning away from traditional RL approaches in motion mimicking through the employment of differentiable physics simulators. The effectiveness demonstrated in reducing training time and enhancing learning stability establishes a firm foundation for future work in the intersection of character animation and differentiable simulations. As the research landscape evolves, DiffMimic may serve as a pivotal tool in advancing autonomous systems that learn complex physical tasks both efficiently and reliably.