Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration.

For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.

Robotically Embodied Biological Neural Networks to Investigate Haptic Restoration with Neuroprosthetic Hands.

Neuroprosthetic limbs reconnect severed neural pathways for control of (and increasingly sensation from) an artificial limb. However, the plastic interaction between robotic and biological components is poorly understood. To gain such insight, we developed a novel noninvasive neuroprosthetic research platform that enables bidirectional electrical communications (action, sensory perception) between a dexterous artificial hand and neuronal cultures living in a multichannel microelectrode array (MEA) chamber. Artificial tactile sensations from robotic fingertips were encoded to mimic slowly adapting (SA) or rapidly adapting (RA) mechanoreceptors. Afferent spike trains were used to stimulate neurons in a region of the neuronal culture. Electrical activity from neurons at another region in the MEA chamber was used as the motor control signal for the artificial hand. Results from artificial neural networks (ANNs) showed that the haptic model used to encode RA or SA fingertip sensations affected biological neural network (BNN) activity patterns, which in turn impacted the behavior of the artificial hand. That is, the exhibited finger tapping behavior of this closed-loop neurorobotic system showed statistical significance (p<0.01) between the haptic encoding methods across two different neuronal cultures and over multiple days. These findings suggest that our noninvasive neuroprosthetic research platform can be used to devise high-throughput experiments exploring how neural plasticity is affected by the mutual interactions between perception and action.

Shape memory alloy tube actuators inherently enable internal fluidic cooling for a robotic finger under force control.

This paper presents the design, control and evaluation of a novel robotic finger actuated by shape memory alloy (SMA) tubes which intrinsically afford an internal conduit for fluidic cooling. The SMA tubes are thennomechanically programmed to flex the robotic finger when Joule heated. A superelastic SMA plate provides a spring return motion to extend the finger when cooling liquid is pumped through the internal channel of the SMA tube actuators. The mechanical design and nonlinear force controller are presented for this unique robotic finger. Sinusoidal and step response experiments demonstrate excellent error minimization when operated below the bandwidth which was empirically determined to be 6 rad s-1. Disturbance rejection experiments are also performed to demonstrate the potential to minimize externally applied forces. This method of internal liquid cooling of Joule heated SMA tubes simultaneously increases the system bandwidth and expands the potential uses of SMA actuators for robotic applications. The results show that this novel robotic finger is capable of precise force control and has a high strength to weight ratio. The finger can apply a force of 4.35 N and has a mass of 30 g. Implementing this design into wearable prosthetic devices could enable lightweight, high strength applications previously not achievable.

Force and Pressure Control of Soft Robotic Actuators.

Soft Robotic Actuators (SRAs) have piqued the interest of researchers in recent years. SRAs are generally constructed of soft elastomers and use air or water as a mean of actuation. Due to the inherent properties of these actuators, they are ideal for HumanRobot Interactions (HRI), exoskeletons for rehabilitation and for grasping delicate objects. Since SRA's are actuated using a fluid, being able to effectively control the rate of actuation, pressure and the force applied is necessary so that the actuator and the object being grasped does not get damaged. This paper aims to evaluate three types of controllers, an open-loop controller, pressure-feedback controller, and a force-feedback controller, all used to control an SRA. A custom test stand was built to hold the SRA and test it with all three controllers. The pressure-feedback controller was set to limit the pressure to 8.9 kPa and the force was limited to 0.147 N in the force-feedback controller. Since the open-loop controller had no feedback, the SRA was actuated at a specified frequency while force and pressure measurements were taken. The force-feedback and the pressure-feedback controllers accurately controlled the actuators and the open loop-controller was able to actuate the SRA reliably.

Robotic Finger Force Sensor Fabrication and Evaluation Through a Glove.

This force-feedback approach compares the effect on the sensing ability through a worn glove of the force application of an i-Limb Ultra robotic hand for several experimental scenarios. A Takktile sensor was integrated into a fabricated fingertip to measure the applied force of the i-Limb Ultra. A controller was then designed using MATLAB/Simulink to manipulate the finger motion of the i-Limb to apply force to an external load cell. Testing was performed to check the force measurements and sensing ability/quality for two cases: hand with no glove and hand with a nitrile glove. Each of these scenarios were tested by applying fingertip force in 3 different modes: open/close with no contact, continuous tapping and constant force.