Sim-to-Real for Soft Robots Using Differentiable FEM: Recipes for Meshing, Damping, and Actuation

Dubied, Mathieu; Michelis, Mike Y.; Spielberg, Andrew; Katzschmann, Robert K.

doi:10.1109/lra.2022.3154050

Cited by 24 publications

(10 citation statements)

References 36 publications

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“…Although there are many successful examples of soft robot modelling and simulation using this technique [ 56 , 57 , 58 ], with errors near 5

, its large computational cost often makes its use in real-time closed control loops unfeasible. It is necessary, if acceptable simulation times are to be achieved, to use reduced-order models, such as done in [ 59 , 60 , 61 ].…”

Section: Related Workmentioning

confidence: 99%

Model-Free Control of a Soft Pneumatic Segment

García-Samartín,

Molina-Gómez,

Barrientos

2024

Biomimetics

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Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL’s performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments.

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“…Although there are many successful examples of soft robot modelling and simulation using this technique [ 56 , 57 , 58 ], with errors near 5

Section: Related Workmentioning

confidence: 99%

Model-Free Control of a Soft Pneumatic Segment

García-Samartín,

Molina-Gómez,

Barrientos

2024

Biomimetics

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“…In addition to black box machine learning techniques, progress in computational and theoretical multiphysics modeling would help improve soft sensor characterization. [213][214][215] Thus, we see soft sensing as a crucial component of task-first soft autonomy, and believe that there are diverse opportunities in fabrication, electronic integration, and modeling to advance the field in the near-term.…”

Section: Soft Sensorizationmentioning

confidence: 99%

Materializing Autonomy in Soft Robots across Scales

Berrueta,

Murphey,

Truby

2023

Advanced Intelligent Systems

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The impressive capabilities of living organisms arise from the way autonomy is materialized by their bodies. Across scales, living beings couple computational or cognitive intelligence with physical intelligence through body morphology, material multifunctionality, and mechanical compliance. While soft robotics has advanced the design and fabrication of physically intelligent bodies, the integration of information‐processing capabilities for computational intelligence remains a challenge. Consequently, perception and control limitations have constrained how soft robots are built today. Progress toward untethered autonomy will require deliberate convergence in how the field codevelops new materials, fabrication methods, and control strategies for soft robots. Here, a new perspective is put forward: that researchers should use tasks alone to impose material and information constraints on soft robot design. A conceptual framework is proposed for a task‐first design paradigm that sidesteps limitations imposed by control strategies. This framework allows emergent synergies between material and information processing properties of soft matter to be readily exploited for task‐capable agents. Particular attention is paid to the scale dependence of solutions. Finally, an outlook is presented on emerging research opportunities for achieving autonomy in future soft robots as large as elephant trunks and as small as paramecia.

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“…For more realistic simulations, finite element methods are the best method to accurately reproduce the dynamics of soft robots [29], [30]. Among them, the Simulation Open Framework Architecture (SOFA) [31] is the most widely used tool and offers fast, real-time control for a diverse range of robot geometries and actuators via traditional and reducedorder modeling methods [32]- [34].…”

Section: A Soft Robotics Simulationmentioning

confidence: 99%

Sim-To-Real Transfer Learning Approach for Tracking Multi-DOF Ankle Motions Using Soft Strain Sensors

Park

Cho

Park

et al. 2020

IEEE Robot. Autom. Lett.

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Soft robotics is a modern robotic paradigm for performing dexterous interactions with the surroundings via morphological flexibility. The desire for autonomous operation requires soft robots to be capable of proprioception and makes it necessary to devise a calibration process. These requirements can be greatly benefited by adopting numerical simulation for computational efficiency. However, the gap between the simulated and real domains limits the accurate, generalized application of the approach. Herein, we propose an unsupervised domain adaptation framework as a data-efficient, generalized alignment of these heterogeneous sensor domains. A dual cross-modal autoencoder was designed to match the sensor domains at a feature level without any extensive labeling process, facilitating the computationally efficient transferability to various tasks. As a proof-of-concept, the methodology was adopted to the famous soft robot design, a multigait soft robot, and two fundamental perception tasks for autonomous robot operation, involving highfidelity shape estimation and collision detection. The resulting perception demonstrates the digital-twinned calibration process in both the simulated and real domains. The proposed design outperforms the existing prevalent benchmarks for both perception tasks. This unsupervised framework envisions a new approach to imparting embodied intelligence to soft robotic systems via blending simulation.

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Sim-to-Real for Soft Robots Using Differentiable FEM: Recipes for Meshing, Damping, and Actuation

Cited by 24 publications

References 36 publications

Model-Free Control of a Soft Pneumatic Segment

Model-Free Control of a Soft Pneumatic Segment

Materializing Autonomy in Soft Robots across Scales

Sim-To-Real Transfer Learning Approach for Tracking Multi-DOF Ankle Motions Using Soft Strain Sensors

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