MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy
This addresses the challenge of integrating realistic hardware models into optimization routines for robotics and automation, though it appears incremental as it builds on existing co-optimization methods.
The paper tackles the problem of co-optimizing hardware design parameters and control policies by introducing MORPH, a method that uses a differentiable proxy hardware model to enable efficient reinforcement learning, demonstrating it on simulated 2D reaching and 3D manipulation tasks.
We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning. Like most co-optimization methods, MORPH relies on a model of the hardware being optimized, usually simulated based on the laws of physics. However, such a model is often difficult to integrate into an effective optimization routine. To address this, we introduce a proxy hardware model, which is always differentiable and enables efficient co-optimization alongside a long-horizon control policy using RL. MORPH is designed to ensure that the optimized hardware proxy remains as close as possible to its realistic counterpart, while still enabling task completion. We demonstrate our approach on simulated 2D reaching and 3D multi-fingered manipulation tasks.