Evolution of intentional teaching

Only among humans is teaching intentional, socially structured, and symbolically mediated. In this chapter, evidence regarding the evolution of the mindreading and communicative capacities underlying intentional teaching is reviewed. Play, rehearsal, and apprenticeship are discussed as central to the analyses of teaching. We present a series of levels of teaching. First of all, we separate non-intentional from intentional teaching. For non-intentional teaching, we discuss facilitation and approval/disapproval and analyze examples from non-human species. We then distinguish between six levels of intentional teaching: (1) intentional approval/disapproval, (2) drawing attention, (3) demonstrating, (4) communicating concepts, (5) explaining concept relations, and (6) narrating. We hypothesize that level after level has been added during the evolution of teaching. We analyze communicative requirements for the levels, concluding that displaced communication is required for level 4 and symbolic language only for levels 5 to 6. We focus on the role of demonstration and pantomime and argue that pantomime has been instrumental in the evolution of language. We present archaeological evidence for when the different levels of teaching emerge. We argue that learning Oldowan technology requires teaching by demonstration, and that learning Acheulean hand-axe technology requires communicating concepts. It follows that several levels of intentional teaching predate homo sapiens.

An extended DMP framework for robot learning and improving variable stiffness manipulation

Purpose The purpose of this paper is to present a method which enables a robot to learn both motion skills and stiffness profiles from humans through kinesthetic human-robot cooperation. Design Methodology Approach Admittance control is applied to allow robot-compliant behaviors when following the reference trajectories. By extending the dynamical movement primitives (DMP) model, a new concept of DMP and stiffness primitives is introduced to encode a kinesthetic demonstration as a combination of trajectories and stiffness profiles, which are subsequently transferred to the robot. Electromyographic signals are extracted from a human’s upper limbs to obtain target stiffness profiles. By monitoring vibrations of the end-effector velocities, a stability observer is developed. The virtual damping coefficient of admittance controller is adjusted accordingly to eliminate the vibrations. Findings The performance of the proposed methods is evaluated experimentally. The result shows that the robot can perform tasks in a variable stiffness mode as like the human dose in the teaching phase. Originality Value DMP has been widely used as a teaching by demonstration method to represent movements of humans and robots. The proposed method extends the DMP framework to allow a robot to learn not only motion skills but also stiffness profiles. Additionally, the authors proposed a stability observer to eliminate vibrations when the robot is disturbed by environment.