Anticipatory models of human movements and dynamics: the roadmap of the AnDy project

International audienceFuture robots will need more and more anticipation capabilities, to properly react to human actions and provide efficient collaboration. To achieve this goal, we need new technologies that not only estimate the motion of the humans, but that fully describe the whole-body dynamics of the interaction and that can also predict its outcome. These hardware and software technologies are the goal of the European project AnDy. In this paper, we describe the roadmap of AnDy, which leverages existing technologies to endow robots with the ability to control physical collaboration through intentional interaction. To achieve this goal, AnDy relies on three technological and scientific breakthroughs. First, AnDy will innovate the way of measuring human whole-body motions by developing the wearable AnDySuit, which tracks motions and records forces. Second, AnDy will develop the AnDyModel, which combines ergonomic models with cognitive predictive models of human dynamic behavior in collaborative tasks, learned from data acquired with the AnDySuit. Third, AnDy will propose AnDyControl, an innovative technology for assisting humans through pre-dictive physical control, based on AnDyModel. By measuring and modeling human whole-body dynamics, AnDy will provide robots with a new level of awareness about human intentions and ergonomy. By incorporating this awareness on-line in the robot's controllers, AnDy paves the way for novel applications of physical human-robot collaboration in manufacturing, health-care, and assisted living

Shared Decision Making in a Collaborative Task with Reciprocal Haptic Feedback - an Efficiency-Analysis

Abstract — When robots leave industrial settings, they have to be designed allowing intuitive communication with the humans they interact with. The current paper focuses on collaboration in kinesthetic tasks. Herein, we investigate decision situations. This way, the need of communication between partners can be addressed. The current paper introduces for the first time an experimental paradigm which allows studying the effect of decision making in haptic collaboration. Because reciprocal haptic feedback is challenging to provide, we analyze its efficiency in human-human collaboration to understand when it is worth to invest in this additional modality. A one degree of tracking experiment with two human partners revealed that the additional physical effort accompanying reciprocal haptic feedback is directly transformed into higher performance (compared to a control condition without reciprocal haptic feedback). Thus, the presented results motivate further research on the nature of the haptic negotiation between human partners to achieve the same performance benefits in kinesthetic collaboration with robotic partners. I