![]() One approach towards building more flexible and domain-independent robot grasping systems is to employ learning to adapt the robot's perceptual and motor system to the task. Reliable vision-based grasping has proved elusive outside of controlled environments. Focusing on the issue of manual action control, we illustrate some results in the context of grasping with a five-fingered anthropomorphic robot hand. Working towards this goal, we translate our findings in studies of motor control in humans into models that can guide the implementation of cognitive robot architectures. ![]() Among the key issues to be addressed is the question how structured representations can arise during skill acquisition and how the underlying processes can be understood sufficiently succinctly to replicate them on robot platforms. To simulate action implementation we discuss challenges and issues that arise when we try to replicate complex movement abilities in robots. It is concluded that such movement representations might provide the basis for action implementation and action control in skilled voluntary movements in the form of cognitive reference structures. Results from different lines of research showed that not only the structure formation of mental representations in long-term memory but also chunk formation in working memory are built up on BACs and relate systematically to movement structures. These BACs are cognitive tools for mastering the functional demands of movement tasks. Basic action concepts (BACs) are identified as major building blocks on a representation level. This paper examines the cognitive architecture of human action, showing how it is organized over several levels and how it is built up. The notion that grasping movements are strongly influenced in The analysis of mental representation of grasps. Small objects and larger objects was also found in the results of Objects were clearly separated from grasps used for mediumĪnd larger objects, and a clear separation of grasps used for ![]() Variance was described by the first two PCs, and this rose toĩ2% when a third PC was added. The movement it is possible to distinguish different end grasps Structure Dimensional Analysis in order to draw comparisonsīetween the observed physical synergies and mental represen. Ing movements using a hierarchical sorting paradigm called Top of this we also analysed mental representations of grasp. Kinematic data was analyzed using principal componentĪnalysis (PCA) in order to extract movement synergies. Movements towards spherical objects that systematically varied We investigated hand kinematics of grasping Shifts in the field, discuss what we consider major challenges and point out promising directions for future research. Some of the manifold connections between manual actions and cognitive functions, review some recent developments and paradigm To substantiate that claim, we present and discuss We envisage manual intelligence as a “Rosetta stone” for robot cognition. Of interaction may make it much more approachable for analysis than other, “higher level” aspects of intelligence. Of objects,actions, and the acquisition of new skills, while the rich grounding of manual intelligence in the physical level We argue that a thorough understanding of manual intelligence will be basic for our concepts Research field, connecting robotics research with advances in the cognitive and brain sciences about the representation and That of human hands in a realistic fashion, manual intelligence for robots is rapidly emerging as an exciting interdisciplinary With the recent advent of anthropomorphic hand designs whose configuration space begins to approximate ![]() A major unsolved problem is to provide robots with sufficient manual intelligence so that they can seamlessly interact with environments made for humans, where almost all objects have been designed for beingĪcted upon by human hands.
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