Learning robotic eye-arm-hand coordination from human demonstration: a coupled dynamical systems approach

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varies between individuals. When passing a fragile object, risk-averse individuals may adopt a larger margin by following the longer path than risk-prone people do. However, it is not known whether this variation is associated with a personalized cost function used for the individual optimal control policies and how it is represented in our brain activity. This study investigates whether such individual variations in evaluation criteria during reaching results from differentiated weighting given to energy minimization versus comfort, and monitors brain error-related potentials (ErrPs) evoked when subjects observe a robot moving dangerously close to a fragile object. Seventeen healthy participants monitored a robot performing safe, daring and unsafe trajectories around a wine glass. Each participant displayed distinct evaluation criteria on the energy efficiency and comfort of robot trajectories. The ErrP-BCI outputs successfully inferred such individual variation. This study suggests that ErrPs could be used in conjunction with an optimal control approach to identify the personalized cost used by CNS. It further opens new avenues for the use of brain-evoked potential to train assistive robotic devices through the use of neuroprosthetic interfaces.

Human-Inspired Robotic Eye-Hand Coordination Enables New Communication Channels Between Humans and Robots

This paper concerns human-inspired robotic eye-hand coordination algorithms using custom built robotic eyes that were interfaced with a Baxter robot. Eye movement was programmed anthropomorphically based on previously reported research on human eye-hand coordination during grasped object transportation. Robotic eye tests were first performed on a component level where accurate position and temporal control were achieved. Next, 11 human subjects were recruited to observe the novel robotic system to quantify the ability of robotic eye-hand coordination algorithms to convey two kinds of information to people during object transportation tasks: first, the transported object's delivery location and second, the level of care exerted by the robot to transport the object. Most subjects correlated decreased frequency in gaze fixations on an object's target location with increased care of transporting an object, although these results were somewhat mixed among the 11 human subjects. Additionally, the human subjects were able to reliably infer the delivery location of the transported object purely by the robotic eye-hand coordination algorithm with an overall success rate of 91.4%. These results suggest that anthropomorphic eye-hand coordination of robotic entities could be useful in pedagogical or industrial settings.