Fully 3D printable robot hand equipped with soft tactile sensors are developed.

The developed robotic hand consists solely of 3D printed parts and commercial motors, with a total cost of less than $400.

The tactile sensors are based on pneumatic and capacitive sensors and are capable of detecting not only sensitive forces but also the presence of nearby conductive objects.

KIMLAB demonstrated the mobile object manipulation operator (MOMO) at IROS 2023, Detroit, US.

This demonstration showcased an innovative modular mobile manipulator system, incorporating three pluggable mounts that can utilize 6-DOF arms and a sensor module for different configurations, thereby enhancing the system’s capabilities beyond those of a traditional serving robot.

The primary objectives of the system encompass the autonomous removal of obstructions from the floor and seamless delivery to human recipients without any human intervention.

Soft pneumatic robotic skin is developed for plug-and-playable robotic arm system (PAPRAS) by KIMLAB. The developed robotic skin was seamlessly integrated with the robot from both hardware and software pespectives. 

The developed soft sensing pad is cheap and easy to produce as it is entirely 3D printable. Also, the sensing electronics are design to be compatible with ROS, making the system highly accessible to everyone.

Soft pad's internal pressure changes due to tactile stimuli such as force and light touch. The measured signals are divided into low and high frequencies and were utilized for interaction or communication. 

The developed system was demonstrated to be capable of effectively handling situations such as joint entrapment and multiple contacts, thereby realizing safe and contact-rich pHRI. 

Skin-inspired multi-layer structure is fabricated using silicone elastomer and ionic hydrogel, which show squish yet durable properties.

The ionic hydrogel layer is used as a medium for tactile sensing since it can transmit alternating electric currents and vibrations.

The tomographic imaging methods are utilized to reconstruct a multi-modal tactile image from measurement data.

A convolutional neural network allows the robotic skin to classify the type of touch by extracting the spatio-temporal pattern of the tactile stimuli.

The developed robotic skin can be repaired using chitosan topohesive and silicone adhesive, even after severe damage (incision).

The sensorized prosthesis was implemented as a proof-of-concept

We developed a method to obtain an optimal electrode arrangement for ERT-based robotic skin.

The electrode arrangement is iteratively updated to maximize the minimum current density of the ERT-based robotic skin.

Each electrode spreads out like a gas molecule inside the sensing domain, forming a grid-like arrangement adapted to the sensor's geometry.

A number of electrodes can be freely adjusted to meet design requirements, owing to the automated design process.

We implemented an ERT-based robotic skin with optimal design, and demonstrated physical human-robot interaction (pHRI).

The performance of ERT-based sensors is improved by increasing the number of electrodes, but the number of measurements and the computational cost also increase.

We propose an adaptive and optimal measurement algorithm for ERT-based tactile sensors; the measurement pattern consists of a base pattern and local patterns.

The tactile events are detected by a base pattern that maximizes the distinguishability of local conductivity changes.

A set of local patterns are selectively recruited near the stimulated region to acquire more detailed information.

The algorithm was implemented with a field-programmable gate array (FPGA).

We ERT-based tactile sensing on the cylindrical surface of the robot

The electrodes are evenly placed on the robot surface, and seamlessly integrated onto the robot surface

The CNT-based conductive surface formed by spray coating

The  3D shell-shape simulation model is used, and the deep neural network is utilized to compensate nonlinear behavior of the sensor

We developed soft robotic skin based on Electrical resistance tomography.

It shows higher spatial resolution due to the multiple internal electrodes.

We utilized a conductive fabric-based transduction mechanism.

We developed a soft tactile sensor using a CNT-coated porous PDMS and an interference reduction structure

It shows low hysteresis due to the CNT-coated porous PDMS.

It shows low interference due to the interference reduction structure.

It can be customized to have a curved shape of the robotic body.

We developed robotic skin that can measure the tactile information on the hand and bending of the finger

The CNT-silicone rubber mixture is utilized as a piezoresistive material

Electrical Impedance Tomography (EIT) based scanning mechanism

A two-dimensional ground reaction force (GRF) measurement system is developed by using optical sensors

We reduce the interference by implementing an interference-free structure

We could measure and analyze the gait phase pattern of the human by attaching the sensor to shoes

The wearable system is capable of Bluetooth communication