Shaping haptics, meeting humans: data-clustering algorithms in human-robot-collaborations
(RWTH Aachen University)
Gabriele Gramelsberger (RWTH Aachen University)
Paper short abstract:
Data-Clustering algorithms are essential in human-robot-collaborations. However, they have been treated marginally so far when it comes to the social dimension of robots. The paper shows how technical and social dimensions intertwine in the modeling of haptics in human-robot collaborations.
Paper long abstract:
In the last couple of years, the notion of the social in "social robots" has become highly disputed. One the one hand, most articles from robotics emphasize aspects of humanoid design and companion-like behavior as social factors. On the other hand, scholars from the Social Studies of Science focused upon the technological situatedness of these non-human-objects in everyday practices - whether as a maintenance of bodily interactions (M. Alač) or through the constitution of a dialogical space in a network of agents (R. Jones). In all these approaches, however, the impact of data-based algorithms for the creation of "the social" has been treated marginally. A good example for this is the most important sense for human-robot-collaboration: haptics. Focusing on case studies coming from the coding of haptics for human-robot collaborations, the paper argues that data-clustering algorithms are highly dependent on both the engineering ideal of an "unsupervised learning algorithm" and the formalized expectation of social responsiveness. In order to structure the data of the robots' sensors and to process a socially anticipated movement, the algorithm constructs a virtual human, a human to come, as a formalization of the social responsive body. Therefore, this construction of algorithms should be considered as both - a factor of computationally driven models of bodily interaction and a social practice of defining the virtual human to come. This means to design the human as the environment of the robot.
The power of correlation and the promises of auto-management. On the epistemological and societal dimension of data-based algorithms