Bayesian Morphology Optimization for Musculoskeletal Systems

Abstract

In this study, we focus on enhancing the policy of a musculoskeletal arm to develop grasping abilities for objects of varying weights. The agent is modeled using MyoSuite, a platform with realistic biomechanics where muscles drive skeletal movement. We observed that optimizing only the control policy is insufficient for handling heavy object grasping, highlighting the limitations of traditional control-focused approaches. To address this issue, we shift our focus to muscle development by optimizing the arm’s muscle parameters. However, this remains challenging for two main reasons. First, the high dimensionality of the muscle parameter space makes it difficult to find optimal designs. Second, evaluating new muscle configurations requires training a control policy, leading to high computational costs. To tackle these challenges, we adopt two strategies. First, we simplify the problem by optimizing only the stiffness parameters, as they have the greatest impact on grasping performance. Second, we apply the Bayesian Morphology Optimization Method (BMO) to efficiently search the parameter space. Compared to genetic algorithms(GA), BMO finds better solutions with fewer evaluations. Experimental results show that BMO achieves similar rewards with 20% fewer iterations than GA and improves the success rate by 10%. In summary, muscle optimization provides an effective solution for grasping tasks, and BMO demonstrates efficient, robust, and generalizable performance in optimizing muscle parameters for such tasks.