Development and Grasp Stability Estimation of Sensorized Soft Robotic Hand

This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.

Coculture with hiPS-derived intestinal cells enhanced human hepatocyte functions in a pneumatic-pressure-driven two-organ microphysiological system

Examining intestine–liver interactions is important for achieving the desired physiological drug absorption and metabolism response in in vitro drug tests. Multi-organ microphysiological systems (MPSs) constitute promising tools for evaluating inter-organ interactions in vitro. For coculture on MPSs, normal cells are challenging to use because they require complex maintenance and careful handling. Herein, we demonstrated the potential of coculturing normal cells on MPSs in the evaluation of intestine–liver interactions. To this end, we cocultured human-induced pluripotent stem cell-derived intestinal cells and fresh human hepatocytes which were isolated from PXB mice with medium circulation in a pneumatic-pressure-driven MPS with pipette-friendly liquid-handling options. The cytochrome activity, albumin production, and liver-specific gene expressions in human hepatocytes freshly isolated from a PXB mouse were significantly upregulated via coculture with hiPS-intestinal cells. Our normal cell coculture shows the effects of the interactions between the intestine and liver that may occur in vivo. This study is the first to demonstrate the coculturing of hiPS-intestinal cells and fresh human hepatocytes on an MPS for examining pure inter-organ interactions. Normal-cell coculture using the multi-organ MPS could be pursued to explore unknown physiological mechanisms of inter-organ interactions in vitro and investigate the physiological response of new drugs.

Humanoid Lower Limb: Design, Analysis, Observer-Based Fuzzy Adaptive Control and Experiment

With flexibility similar to human muscles, pneumatic artificial muscles (PAMs) are widely used in bionic robots. They have a high power-mass ratio and are only affected by single-acting pneumatic pressure. Some robots are actuated by a pair of PAMs in the form of antagonistic muscles or joints through a parallel mechanism. The pneumatic pressure and length of PAMs should be measured simultaneously for feedback using a pressure transducer and draw-wire displacement sensor. The PAM designed by the FESTO (10 mm diameter) is too small to install a draw-wire displacement sensor coaxially and cannot measure muscle length change directly. To solve this problem, an angular transducer is adopted to measure joint angles as a whole. Then, the inertia of the lower limb is identified, and observer-based fuzzy adaptive control is introduced to combine with integrated control of the angular transducer. The parameters of the fuzzy control are optimized by the Gaussian basis neural network function, and an observer is developed to estimate the unmeasured angular accelerations. Finally, two experiments are conducted to confirm the effectiveness of the method. It is demonstrated that piriformis and musculi obturator internus act as agonistic muscle and antagonistic muscles alternatively, and iliopsoas is mainly responsible for strengthening because of the constant output force. Piriformis has a greater influence on yaw and roll angles, while musculi obturator internus is the one that influences the pitch angle the most. Due to joint friction, the dead zone of the high-speed on-off valve, lag of compressed air in the trachea, and coupling among angles are very difficult to realize precise trajectory tracking of the pitch, yaw, and roll angles simultaneously.