Kinematics Transformer: Solving The Inverse Modeling Problem of Soft Robots using Transformers
This addresses the challenge of controlling soft robots in fragile environments like marine settings, though it appears incremental as it builds on existing transformer methods for a specific domain.
The paper tackled the inverse kinematics problem for soft robotic limbs by proposing the Kinematics Transformer, which recasts it as a sequential prediction problem using transformers, achieving better accuracy and precision compared to a baseline feed-forward neural network in simulations.
Soft robotic manipulators provide numerous advantages over conventional rigid manipulators in fragile environments such as the marine environment. However, developing analytic inverse models necessary for shape, motion, and force control of such robots remains a challenging problem. As an alternative to analytic models, numerical models can be learned using powerful machine learned methods. In this paper, the Kinematics Transformer is proposed for developing accurate and precise inverse kinematic models of soft robotic limbs. The proposed method re-casts the inverse kinematics problem as a sequential prediction problem and is based on the transformer architecture. Numerical simulations reveal that the proposed method can effectively be used in controlling a soft limb. Benchmark studies also reveal that the proposed method has better accuracy and precision compared to the baseline feed-forward neural network