In the past few decades, multijoint open-chain robotic arms have been utilized in a diverse set of applications, ranging from robotic surgeries to space debris mitigation . The control of robotic arms is currently dominated by proportional, integral, and derivative (PID)-based modeling. Such a model aims to accurately represent the controlled system. It would capture the effect of external perturbations and the system’s internal dynamics on its ability to move. Thus, it provides signals for movement control, given a desired location. However, in a human collaborative-assistive setting, the controller should consider kinematic changes in the system, such as object manipulation of an unknown dimension or at an unknown gripping point. Neuromorphic systems have been shown to outperform PID-based implementation of the required nonlinear adaptation, particularly in their ability to handle high degree-of-freedom systems. One possible implementation for neuromorphic control is the recurrent error-driven adaptive control hierarchy (REACH) model proposed by DeWolf and colleagues . REACH is powered by PES, implemented using NEF, realized within the Nengo development environment, and open-sourced by Applied Brain Research Inc. The model has been demonstrated to control a three-link, nonlinear arm through complex paths, including handwritten words and numbers. It can adapt to environmental changes (e.g., an unknown force field) and changes to the physical properties of the arm (e.g., tear of joints) (Figure 8, A ).
To demonstrate the applicability of our circuit design, we used the OZ and our learning core emulator to implement REACH on a 6 degree-of-freedom robotic arm in a physical simulator (Figure 8, B ). We have implemented two simulations. In the first simulated scenario, we applied an external force field on the robot’s joints. The arm, therefore, cannot accurately reach the specified target points without adaptation, as the internal joint’s dynamic does not consider unknown perturbations (Figure 8, C ). In the second simulated scenario, we used adaptive signals, allowing the arm to adjust its behavior in real time using PES. We used the classical REACH Nengo-driven model and our circuit emulator to power the arm adaptation. Both Nengo and OZ show similar adaptation patterns, allowing the arm to reach the desired target points accurately while an external force field is applied (Figure 8, D ).