An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation

Abstract

Abstract-Physical cooperation with humans greatly enhances the capabilities of robotic systems when leaving standardized industrial settings. Our novel cognition-enabled control framework presented in this paper enables a robotic assistant to enrich its own experience by acquisition of human task knowledge during joint manipulation. Our robot incrementally learns semantic task structures during joint task execution using hierarchically clustered Hidden Markov Models. A semantic labeling of recognized task segments is acquired from the human partner through speech. After a small number of repetitions, the robot uses an anticipated task progress to generate a feed-forward set point for an admittance feedback control scheme. This paper describes the framework and its implementation on a mobile bi-manual platform. The evolution of the robot's task knowledge is presented and discussed. Finally, the cooperation quality is measured in terms of the robot's task contribution.

A review on interaction control for contact robots through intent detection

“…The control community uses adaptive control and iterative learning control to estimate x * h and adapt x * [105][106][107][108][109][110], while the ML community uses probabilistic models to encode x * h and decode it for adaptation of x * [57,60,111,112]. These two approaches may have complementary advantages: the modelling in control theory has more intuitive physical meanings and the system performance can be analysed in rigour; while the ML method takes system uncertainties into account, which are inevitable in a human-robot interaction system.…”

“…[ [105][106][107][108][109][110]: same as human motion target, adaptive/iterative learning control [57,60,111,112]: probabilistic models [47]: interdependent motion targets [47]: independent motion targets [47,102,103]: opposite motion target Robot control command (ξ 0 = u0, ξ h = u h , ξ = u) [118,119,121,122]: minimization of human control command [100,[115][116][117]: supplement to human control command (u0 = 0) [118,119,121,122]: interdependent control commands (u0 ̸ = 0) [118,119,121,122]: independent control commands Robot gain (ξ 0 = K0,…”

A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot

“…In Rozo et al (2015), Gaussian functions are used to classify and learn cooperative human-robot skills in the context of object transport. In Medina et al (2011), a method is proposed using Markov models to increase the experience of a manipulator robot in collaborative tasks with humans; the control adapts and improves cooperation through user speech commands and repetitive haptic training tasks.…”