Reviews: Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations

Summary The paper proposes a differentiable pipeline that can jointly learn a latent space representation (via variational autoencoder) for controlling soft robots and optimize for the controller and the soft robot parameters for tasks in simulation, such as making a soft 2D robot walk forward as fast as possible. The work is made possible by using a differentiable hybrid-particle-grid based soft material physics simulator. The authors provided insightful details on the alternative minimization scheme for training the autoencoder, the controller neural network, and the robot parameters in tandem. The proposed framework was evaluated on 5 simulated experiments that show controllers using the learned representation outperforms ones using the baseline representation obtained via k-means clustering. Review While the performance of the system is impressive, the motivation of the approach is not well-communicated in 3 folds: In discussing the proposed hybrid-particle-grid based soft robot representation vs finite element methods, the authors claim that the high "degrees of freedom of finite element methods is impractical for most modern control algorithms."