Liquid phase change pouch actuators (liquid pouch motors) hold great promise for a wide range of robotic applications, from artificial organs to pneumatic manipulators for dexterous manipulation. However, the usability of liquid pouch motors remains challenging due to the nonlinear intrinsic properties of liquids and their highly dynamic implications for liquid-gas phase changes, which complicate state modeling and estimation. To address these issues, we propose a reservoir computing-based method for modeling the inflation states of a customized liquid pouch motor, which serves as an actuator, featuring four Peltier heating junctions. We use a motion capture system to track the landmark movements on the pouch as a proxy for its volumetric profile. These movements represent the internal liquid-gas phase changes of the pouch at stable room temperature, atmospheric pressure, and in the presence of electrical noise. The motion coordinates are thus learned by our reservoir computing framework, PhysRes, to model the states based on prior observations. Through training, our model achieves excellent results on the test set, with a normalized root mean squared error of 0.0041 in estimating the states and a corresponding volumetric error of 0.0160%. To further demonstrate how such actuators could be implemented in the future, we also design a dual-pouch actuator-based robotic gripper to control the grasping of soft objects. Our design and source code are available at: https://github.com/tatung/liquidpouch_reservoir.

This paper presents a new approach to implement wearable haptic devices using Shape Memory Alloy (SMA) wires. The proposed concept allows building silent, soft, flexible and lightweight wearable devices, capable of producing the sense of pressure on the skin without any bulky mechanical actuators. We explore possible design considerations and applications for such devices, present user studies proving the feasibility of delivering meaningful information and use nonlinear autoregressive neural networks to compensate for SMA inherent drawbacks, such as delayed onset, enabling us to characterize and predict the physical behavior of the device.

This paper presents two innovative design solutions for thermal feedback displays in virtual environments. First solution is aiming to eliminate or decrease the time delay between the user action and onset of the thermal feedback using Machine Learning for user motion prediction. Second is the design of compact but efficient water cooling system necessary to provide cold sensations using peltier elements. Presented thermal display is wearable and battery powered.