Analysis of learning the bimanual control of (tele)operating joint space controlled robotic arms with 4 degrees of freedom using the two-timescales power law of learning.

Training costs for operators of robotic arms in forestry and construction are high. A systematic analysis of skill development can help to make training more efficient. This research focuses on motor skill development by investigating the bimanual control of a four-DoF robotic arm. The two-time scale power law of learning was used to identify difficulties in control learning. Ten participants acquired the control of the robotic arm in a simulator over ten sessions within seven weeks. Eight movement targets were presented in each of six blocks per session, comprising 432 robotic arm movements. The results suggest that learning varies for each joystick axis, with control of the elbow joint showing the highest learning gain. The base and shoulder joints showed similar learning gains. The wrist joint showed mixed results in terms of use or disuse. Performance increased with retention, suggesting that a longer period of consolidation aided skill acquisition. Practitioner summary: A shortage of skilled operators, costly, and extensive training of heavy machine operators in robotic arm control requires to revisit control skill learning. This study showed that focus of training ought to be shifted to specific joints and training requires to emphasise longer resting periods between training sessions. [ABSTRACT FROM AUTHOR]