Grasping and manual interaction for robots so far has largely been approached with an emphasis on physics and control aspects. Given the richness of human manual interaction, we argue for the consideration of the wider field of "manual intelligence" as a perspective for manual action research that brings the cognitive nature of human manual skills to the foreground. We briefly sketch part of a research agenda along these lines, argue for the creation of a manual interaction database as an important cornerstone of such an agenda, and describe the manual interaction lab recently set up at CITEC to realize this goal and to connect the efforts of robotics and cognitive science researchers towards making progress for a more integrated understanding of manual intelligence. From Robots to Manual IntelligenceProgress in mechatronics, sensing and control has made sophisticated robot hands possible whose potential for dexterous operation is at least beginning to approach the superb performance of human hands [1][2][3]. The increasing availability of these hands, together with sophisticated, physicsbased simulation software, has spurred a revival of the field of anthropomorphic hand control in robotics, whose ultimate goal is to replicate the abilities of human hands to handle everyday objects in flexible ways and in unprepared environments.The authors are cooperating within the Bielefeld Excellence Cluster Cognitive Interaction Technology (CITEC) and the Bielefeld Institute for Cognition and Robotics (CoR-Lab).

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Abstract:In this paper, we apply standard decomposition approaches to the problem of finding local correlations in multi-modal and high-dimensional grasping data, particularly to correlate the local shape of cup-like objects to their associated local grasp configurations. We compare the capability of several decomposition methods to establish these task-relevant, inter-modal correlations and indicate how they can be exploited to find potential contact points and hand postures for novel, though similar, objects.