A concurrent learning (CL)-based parameter estimator is developed to identify the unknown parameters in a nonlinear system. Unlike state-of-the-art CL techniques that assume knowledge of the state derivative or rely on numerical smoothing, CL is implemented using a dynamic state-derivative estimator. A novel purging algorithm is introduced to discard possibly erroneous data recorded during the transient phase for CL. Asymptotic convergence of the error states to the origin is established under a persistent excitation condition, and the error states are shown to be uniformly ultimately bounded under a finite excitation condition.

Soft robots are notoriously hard to control. This is partly due to the scarcity of models and simulators able to capture their complex continuum mechanics, resulting in a lack of control methodologies that take full advantage of body compliance. Currently available methods are either too computational demanding or overly simplistic in their physical assumptions, leading to a paucity of available simulation resources for developing such control schemes. To address this, we introduce Elastica, an open-source simulation environment modeling the dynamics of soft, slender rods that can bend, twist, shear, and stretch. We couple Elastica with five state-of-the-art reinforcement learning (RL) algorithms (TRPO, PPO, DDPG, TD3, and SAC). We successfully demonstrate distributed, dynamic control of a soft robotic arm in four scenarios with both large action spaces, where RL learning is difficult, and small action spaces, where the RL actor must learn to interact with its environment. Training converges in 10 million policy evaluations with near real-time evaluation of learned policies.