The laboratory is located in the new capital of Kazakhstan at Robotics Department of Nazarbayev University  

Control – oriented research direction that tackles general problems of steering a physical contact between a robot arm and the environment via tactile sensing arrays. Firstly, it is important to understand what to control and how to derive these “what to control”.  We describe tactile features representing this “what” and robot arm control algorithm in our paper (Touch driven controller and tactile features for physical interactions, Robotics and Autonomous Systems, 2020)

Fabrication – oriented direction that focuses on a general use of transparent silicone and image cameras connected via plastic optical fibers in detecting physical contacts between the robot end-effectors and the environment. First approach used reflection, which was created using the magnetron spattering method, Color-Coded tactile sensor ICRA2019 and the second method was based on detecting changes of color due to mechanical shear and strain Shear and Protrusion Sensor (RA-L with ICRA2020)

 

Fabrication – oriented direction that focuses on investigating methods of using of magnets and magnetic field sensors as tactile sensing devices. The use of the Hall-effect sensor that detectects the distance to the magnet attached onto a spring is described in IROS 2018 conference publication:  Magnetic Tactile Sensor.

3. Vibro-tactile slip, slide, texture detection

Slip detection – oriented research direction that mainly focuses on understanding how to detect and vibrations that appear during mutual movements between the robot end-effector and an object. We created the sensing system described in Vibro-tactile sensor, IEEE SII2019, Paris

3.1 Deep Vibro-Tactile Slip Detection and Texture Identification

The state-of-the-art LSTM, FFN, CNN architectures were applied to detect slippage and identify various textures – journal paper (Massalim et al, mdpi sensors, 2020).

4. Deformable Object Recognition

Soft objects are increasing their attention in manipulation – oriented papers. We developed a pipeline to detect soft and rigid objects using a tactile sensing array – conference IEEE SII2019, Paris.

4.1 Granular Object Recognition

Soft objects can be crunchy inside. In this connection, we can detect various soft objects based on their not only softness but also mechanical vibrations. We applied machine learning methods to detect a foreign body in a soft object (Syrymova et al, AIM2020, Boston).

5. Haptic Illusions

Haptics – oriented research direction, in which we aim in developing new combinations of haptic and visual illusions. We show how pseudoo- and haptic illusions can be used to trick a human brain in perceiving soft objects with different stiffness.

RA-L + ICRA2020, Paris

6. Negative Stiffness Structures

Structures design – related research direction that investigates how artificial mezo-structures with negative stiffness properties can be used as active tactile surfaces.

Analytical modelling of a Negative Stiffness Honeycomb is described in IOP Smart Materials and Structures paper.

 

6.1  Linear Negative Stiffness Honeycomb Actuator with Integrated Force Sensing

Application of Negative Stiffness Structures in actuators is described in our AIM2020, Boston conference paper by Galimzhanov et al.