Sim2Real for Soft Robotic Fish via Differentiable Simulation
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Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning the material parameters of our soft robotic fish. On the example of a soft robotic fish, we demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters from quasi-static data via differentiable simulation and apply it to the prediction of dynamic performance. Our method identifies physically plausible Young's moduli for various soft silicone elastomers and stiff acetal copolymers used in creation of our three different robotic fish tail designs. We show that our method is compatible with varying internal geometry of the actuators, such as the number of hollow cavities. Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware to millimeter-accuracy and within 3% error normalized to actuator length. We provide a differentiable and robust estimate of the thrust force using a neural network thrust predictor; this estimate allows for accurate modeling of our experimental setup measuring bollard pull. This work presents a prototypical hardware and simulation problem solved using our differentiable framework; the framework can be applied to higher dimensional parameter inference, learning control policies, and computational design due to its differentiable character.
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Publication status
published
Editor
Book title
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
12598 - 12605
Publisher
IEEE
Event
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
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Methods
Software
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Date created
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Organisational unit
09689 - Katzschmann, Robert / Katzschmann, Robert
02219 - ETH AI Center / ETH AI Center
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