Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion (2011.02404v3)

Abstract: Understanding the gap between simulation and reality is critical for reinforcement learning with legged robots, which are largely trained in simulation. However, recent work has resulted in sometimes conflicting conclusions with regard to which factors are important for success, including the role of dynamics randomization. In this paper, we aim to provide clarity and understanding on the role of dynamics randomization in learning robust locomotion policies for the Laikago quadruped robot. Surprisingly, in contrast to prior work with the same robot model, we find that direct sim-to-real transfer is possible without dynamics randomization or on-robot adaptation schemes. We conduct extensive ablation studies in a sim-to-sim setting to understand the key issues underlying successful policy transfer, including other design decisions that can impact policy robustness. We further ground our conclusions via sim-to-real experiments with various gaits, speeds, and stepping frequencies. Additional Details: https://www.pair.toronto.edu/understanding-dr/.

• The paper demonstrates that sim-to-real transfer of Laikago quadruped policies is achievable without extensive dynamics randomization.
• The authors show that design factors such as low proportional gain and velocity feedback are critical for achieving robust torque-controlled behavior under significant perturbations.
• The study finds that targeted randomization on key parameters, instead of broad randomization, avoids overly conservative policies while enhancing transfer performance.

Insights on Dynamics Randomization for Quadrupedal Locomotion

This paper investigates the role of dynamics randomization in sim-to-real transfer for learning locomotion policies of the Laikago quadruped robot. Previous findings have presented mixed evaluations concerning the necessity of dynamics randomization in achieving effective sim-to-real transfer. This paper presents an in-depth evaluation that challenges the prevalent assumption that dynamics randomization is essential.

The authors conducted extensive experiments to evaluate whether dynamics randomization is a prerequisite for robust sim-to-real transfer. They demonstrated that successful sim-to-real transfer of Laikago quadruped policies is achievable in the absence of dynamics randomization across a variety of gaits and speeds. These results are particularly contradictory to some prior findings, which emphasized the necessity of dynamics randomization for similar robots and gaits. The research identifies several design decisions, notably the choice of proportional gain and sensory observations, as critical factors influencing sim-to-real success.

Key Experimental Findings

Three main experimental findings emerged from this work:

1. Non-Essential Dynamics Randomization: Empirical results underscored that sim-to-real transfer can materialize without dynamics randomization, provided that the learned policies are robust against significant perturbations. The experiments with Laikago showed remarkable robustness against mass and proprioceptive errors, making immediate sim-to-real transfer possible.
2. Design Influences: Implementing a low proportional gain ($k_p$ = 40) affects control policy characteristics, yielding a torque-controlled behavior crucial for tackling perturbations typically encountered during sim-to-real transfer. The inclusion of velocity feedback also proves significant in enhancing stability and control. Conversely, systems trained with high proportional gain and dynamics randomization, without velocity feedback, faced consistent failure during sim-to-real trials.
3. Optimal Randomization Strategy: The authors suggest that unnecessary dynamics randomization can result in overly conservative policies that offer limited practical gains. They advocate for targeted randomization on parameters where significant modeling errors may exist. A notable example is latency, where randomization provided tangible robustness improvements without adverse effects on policy behavior.

Practical and Theoretical Implications

The findings underpin a practical insight: dynamics randomization should be employed judiciously and only when empirically validated against simulations. This conservative approach is essential for computational efficiency and prohibits the degradation of policy performance through superfluous robustness to insignificant dynamics variations.

From a theoretical perspective, the research prompts reconsideration of the necessity and extent of dynamics randomization in robotics reinforcement learning frameworks. By accentuating the importance of alternative design choices such as sensor feedback and control gains, the paper offers a nuanced perspective on bridging the reality gap in robotic locomotion.

Future Developments in AI and Robotics

This paper contributes to the broader discourse on AI in robotics, suggesting pathways for enhancing sim-to-real transfer without overreliance on dynamics randomization. Future research may explore the implications of this approach on more dynamic and complex locomotion tasks, such as running or jumping, to determine if similar principles govern successful policy transfers. The integration of advanced domain-driven randomization techniques could further refine robustness strategies in hybrid simulations.

In conclusion, this research adds a critical voice to the discussion surrounding dynamics randomization, providing empirical evidence for a refined approach to sim-to-real transfer methodologies in quadrupedal robotic systems. Its insights offer significant implications for both practitioners and theoreticians aiming to enhance the robustness and reliability of trained locomotion policies.