Hair care robots can help address labor shortages in elderly care while enabling those with limited mobility to maintain their hair-related identity. We present MOE-Hair, a soft robot system that performs three hair-care tasks: head patting, finger combing, and hair grasping. The system features a tendon-driven soft robot end-effector (MOE) with a wrist-mounted RGBD camera, leveraging both mechanical compliance for safety and visual force sensing through deformation. In testing with a force-sensorized mannequin head, MOE achieved comparable hair-grasping effectiveness while applying significantly less force than rigid grippers. Our novel force estimation method combines visual deformation data and tendon tensions from actuators to infer applied forces, reducing sensing errors by up to 60.1% and 20.3% compared to actuator current load-only and depth image-only baselines, respectively. A user study with 12 participants demonstrated statistically significant preferences for MOE-Hair over a baseline system in terms of comfort, effectiveness, and appropriate force application. These results demonstrate the unique advantages of soft robots in contact-rich hair-care tasks, while highlighting the importance of precise force control despite the inherent compliance of the system. Videos, data, and code are available at moehair.github.io.

Dynamic in-hand manipulation remains challenging for soft robotic systems, which have demonstrated advantages in safe, compliant interactions but struggle with highspeed dynamic tasks. In this work, we present SWIFT, a system for learning dynamic tasks using a soft and compliant robotic hand. Unlike previous works that rely on simulation, quasistatic actions, and precise object models, SWIFT learns to spin a pen through trial and error using only real-world data and without requiring explicit knowledge of the pen's physical attributes. With self-labeled trials sampled from the real world, SWIFT discovers the set of pen grasping and spinning primitive parameters that enables a soft hand to spin a pen reliably. After 130 sampled actions per object, SWIFT achieves 10/10 success rate across three pens with different weights and weight distributions, demonstrating generalizability and robustness to changes in object properties. The results highlight the potential for soft robotic end-effectors to perform dynamic tasks. We also demonstrate generalization to different shapes and weights, such as a brush and a screwdriver, with 10/10 and 5/10 success rates, respectively. Videos, data, and code are available at https://soft-spin.github.io.

Shape estimation is crucial for precise control of soft robots. However, soft robot shape estimation and proprioception are challenging due to their complex deformation behaviors and infinite degrees of freedom. Their continuously deforming bodies complicate integrating rigid sensors and reliably estimating its shape. In this work, we present Proprioceptive Omnidirectional End-effector (POE), a tendon-driven soft robot with six embedded microphones. We first introduce novel applications of 3D reconstruction methods to acoustic signals from the microphones for soft robot shape proprioception. To improve the proprioception pipeline’s training efficiency and model prediction consistency, we present POE-M. POE-M predicts key point positions from acoustic signal observations and uses an energy-minimization method to reconstruct a physically admissible high-resolution mesh of POE. We evaluate mesh reconstruction on simulated data and the POE-M pipeline with real-world experiments. Ablation studies suggest POE-M’s guidance of the key points during the mesh reconstruction process provides robustness and stability to the pipeline. POE-M reduced the maximum Chamfer distance error by 23.1 % compared to the state-of-the-art end-to-end soft robot proprioception models and achieved 4.91 mm average Chamfer distance error during evaluation. Supplemental materials, experiment data, and visualizations are available at sites.google.com/view/acoustic-poe.

Pneumatic soft robots present many advantages in manipulation tasks. Notably, their inherent compliance makes them safe and reliable in unstructured and fragile environments. However, full-body shape sensing for pneumatic soft robots is challenging because of their high degrees of freedom and complex deformation behaviors. Vision-based proprioception sensing methods relying on embedded cameras and deep learning provide a good solution to proprioception sensing by extracting the full-body shape information from the high-dimensional sensing data. But the current training data collection process makes it difficult for many applications. To address this challenge, we propose and demonstrate a robust sim-to-real pipeline that allows the collection of the soft robot's shape information in high-fidelity point cloud representation. The model trained on simulated data was evaluated with real internal camera images. The results show that the model performed with averaged Chamfer distance of 8.85 mm and tip position error of 10.12 mm even with external perturbation for a pneumatic soft robot with a length of 100.0 mm. We also demonstrated the sim-to-real pipeline's potential for exploring different configurations of visual patterns to improve vision-based reconstruction results. The code and dataset are available at https://github.com/DeepSoRo/DeepSoRoSim2Real.

In this paper, we present an analytical modeling approach to address the problem of tension loss in a generic variable curvature tendon-driven continuum manipulators (TD-CM) occurring due to the tendon-sheath distributed friction force. Despite the previous approaches in the literature, our presented model and the iterative solution algorithm do not rely on a priori known curvature/shape of the TD-CM and can be implemented on any TD-CM with constant/ variable curvatures with a continuous neutral axis function. The performance of the proposed modeling approach in predicting the distributed tendon tension and tension loss has been evaluated via simulation and experimental studies on a TD-CM with planar bending. Results demonstrate the outstanding and accurate performance of our novel modeling and the proposed solution algorithm.