Soft-arm reinforcement learning drops from three years to three hours: a six-dimensional reduced model trains a tracker with 40% lower error than an RNN policy
Reinforcement-learning control policies for a pneumatically actuated soft arm can be trained entirely on an offline reduced model instead of through repeated interaction with fragile hardware. Soft robots have lacked a reliable, fast simulator, and trial and error on the hardware itself is costly. The arm's dynamics are reduced to a six-dimensional adiabatic spectral submanifold (aSSM), and a polynomial ordinary differential equation with only 6,924 parameters serves as the training environment; a tracking policy trains in three hours and deploys without fine-tuning, with 40% lower tracking error than otherwise identical policies trained on a 4.8-million-parameter RNN or a 1.7-million-parameter transformer.