Dynamic Primitives Facilitate Manipulating a Whip

Abstract

Human dexterity far exceeds that of modern robots, despite a much slower neuromuscular system. Understanding how this is accomplished may lead to improved robot control. The slow neuromuscular system of humans implies that prediction based on some form of internal model plays a prominent role. However, the nature of the model itself remains unclear. To address this problem, we focused on one of the most complex and exotic tools humans can manipulate-a whip. We tested (in simulation) whether a distant target could be reached with a whip using a (small) number of dynamic primitives, whose parameters could be learned through optimization. This approach was able to manage the complexity of an (extremely) high degree-of-freedom system and discovered the optimal parameters of the upper-limb movement that achieved the task. A detailed model of the whip dynamics was not needed for this approach, which thereby significantly relieved the computational burden of task representation and performance optimization. These results support our hypothesis that composing control using dynamic motor primitives may be a strategy which humans use to enable their remarkable dexterity. A similar approach may contribute to improved robot control.

Theoretical Evidence Supporting Harmonic Reaching Trajectories

“…The Passive Motion Paradigm (PMP) has been proposed to overcome this issue by using a passive impedance controller which is robust to information delays [5]- [9]. A similar approach has been recently used to define the inclusion of dynamic primitives via the optimisation of multiple impedance controllers connected in parallel [10], [11]. Nevertheless, all these models still rely on the minimum jerk for planning task-space trajectories.…”

Dynamic Movement Primitives in Robotics: A Tutorial Survey

“…Pfeiffer and Angulo (2015) represented gestures by applying DMPs on REEM robotic platform with 23-DoFs. Nah et al (2020) proposed an approach to optimize DMP parameters in order to deal with the complexity of of high DoF system like a whip. They tested their approach in simulation for 10-, 15-, 20-, and 25-DoFs systems.…”

Highlights from the 30th Annual Meeting of the Society for the Neural Control of Movement

“…A detailed model of the whip dynamics was not needed for this approach. This may be a key simplification that humans leverage to learn complex motor skills, avoiding the need to internalize the detailed dynamic properties of the object being manipulated (31).…”