Unsupervised Sim-to-Real Adaptation of Soft Robot Proprioception using a Dual Cross-modal Autoencoder

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 high-fidelity 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.Comment: 13 pages, 12 figure

Tactile Mapping and Localization from High-Resolution Tactile Imprints

This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct the tactile shape of objects from tactile imprints, and an accurate method for object localization of previously reconstructed objects. The algorithms can be applied to a large variety of 3D objects and provide accurate tactile feedback for in-hand manipulation. Results show that by exploiting the dense tactile information we can reconstruct the shape of objects with high accuracy and do on-line object identification and localization, opening the door to reactive manipulation guided by tactile sensing. We provide videos and supplemental information in the project's website http://web.mit.edu/mcube/research/tactile_localization.html.Comment: ICRA 2019, 7 pages, 7 figures. Website: http://web.mit.edu/mcube/research/tactile_localization.html Video: https://youtu.be/uMkspjmDbq

Teleoperation and Contact Detection of a Waterjet-Actuated Soft Continuum Manipulator for Low-Cost Gastroscopy

Gastric cancer is the third leading cause of cancer deaths worldwide, with most new cases occurring in low and middle income countries, where access to screening programs is hindered by the high cost of conventional endoscopy. The waterjet-actuated HydroJet endoscopic platform was developed as a low-cost, disposable alternative for inspection of the gastric cavity in low-resource settings. In this work, we present a teleoperation scheme and contact detection algorithm that work together to enable intuitive teleoperation of the HydroJet within the confined space of the stomach. Using a geometrically accurate stomach model and realistic anatomical inspection targets, we demonstrate that, using these methods, a novice user can complete a gastroscopy in approximately the same amount of time with the HydroJet as with a conventional endoscope