Differential game theory for versatile physical human–robot interaction

Abstract

The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviors. Here we develop the first interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can: induce a stable interaction between the two partners; precisely identify each other's control law; and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how the new controller can induce different representative interaction strategies.

Optimal robot‐environment interaction using inverse differential Riccati equation

“…xt within the environment model (4). To do this, we first reform model dynamics (4) to be in the form with the state equation in (7). Then, obtain optimal values by employing the presented iDRE method.…”

“…It can address emerging aspects of rehabilitation robotics, surgery robotic systems, haptic rendering, and several fields in human-robot interaction systems [1]. Control in REI systems has been studied to cope with different problems like impedance adaptation [2], impedance learning [3], collaborative manufacturing [4], or assistive human-robot interaction [5][6][7]. However, in most of the research work on REI control, desired trajectories in the task space are given, then tracking problems are addressed [8,9] whereas in several applications of REI, like pick-and-place operations, two-end points are given and the path should be planned according to the desired objective.…”

“…It is also noted that in (27), we assumed the value of the performance parameter S , as in (6), to be zero. Also, it is worth noting that by forming the environment model according to the state system (7), the complete dynamic model of the robot can be obtained as in (3) without linearization of the model.…”

Robots that Take Advantage of Human Trust

“…When both agents know θ r and θ h , this HRI problem is an instance of a deterministic linear-quadratic game [22]. Control strategies that achieve a Nash equilibrium are of the form u t r = −K t r x t and u t h = −K t h x t , where K r , K h ∈ R 1×2 are feedback gain matrices [22], [23]. We note that-within this deterministic LQ game-a pure Nash equilibrium exists and is unique.…”

Design of a 3D Platform for Immersive Neurocognitive Rehabilitation

“…In recent years, computer vision and graphics to support complex frameworks in human-computer interactions (HCI) are becoming increasingly important to address a wide range of application areas [1,2]. Nowadays, in fact, more and more critical tasks see advanced interactions between complex systems and users, or more in general, between intelligent environments and users, so that the latter can interact, more or less voluntarily, with smart and autonomous applications.…”