Get Towards Affordance-Based Robot Control: International PDF

By Verena V. Hafner, Frédéric Kaplan (auth.), Erich Rome, Joachim Hertzberg, Georg Dorffner (eds.)

ISBN-10: 3540779140

ISBN-13: 9783540779148

Today’s cellular robotic belief is inadequate for appearing goal-directedly in unconstrained, dynamic daily environments like a house, a manufacturing facility, or a urban. topic to regulations in bandwidth, computing device energy, and computation time, a robotic has to react to a wealth of dynamically altering stimuli in such environments, requiring swift, selective realization to decisive, action-relevant details of excessive present application. powerful and common engineering equipment for successfully and successfully coupling conception, motion and reasoning are unavailable. fascinating functionality, if any, is presently purely completed via refined robotic programming exploiting area positive factors and specialties, which leaves traditional clients no likelihood of adjusting how the robotic acts.

The function of this quantity - end result of a GI-Dagstuhl Seminar held in Dagstuhl citadel in June 2006 - is to offer a primary assessment at the notion of affordances for the layout and implementation of independent cellular robots performing goal-directedly in a dynamic setting. the purpose is to enhance affordance-based regulate as a mode for robotics. the opportunity of this new method can be proven by way of going past navigation-like projects in the direction of goaldirected self reliant manipulation within the venture demonstrators.

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Additional info for Towards Affordance-Based Robot Control: International Seminar, Dagstuhl Castle, Germany, June 5-9, 2006. Revised Papers

Example text

We note that noise-level in videos is not the only source of tracking errors. , there are 5 free parameters for a boxshape, 3 for a circle), which can be improved by increasing the number and the quality of searches for correct poses at the cost of computational-complexity. We also notice the lack of attention mechanism in our action recognition framework. This results in interpretation of actions involving each target object independently. In Fig. 7 at Frame 305, when actions are analyzed independently the system also indicates that the agent is retracting his hand from the green ball.

T be a time series of observations, where Ot = (fh , fu , fhu , fuv )t is the set of features computed for the scene at frame t. T |g i ) as follows. T T = P (g i ) t=1 at T = P (g i )P (O0 |s0 ) P (Ot |at )P (Ot |st )P (st |st−1 at )P (at |st−1 , g i ) (3) t=a1t=A In Eq. 1, the likelihood is marginalized only over possible maneuvering acts, as we only keep track of the most likely state sequences, which can be estimated at time T . Equation 2 is derived based on the assumption that 1) at depends on st−1 , and 2) given st and at , Ot is independent of s and a at other time frames.

Moreover, a truly flexible robot needs to able to characterise actions so that it can distinguish what is an essential component of the action, and what is superfluous. One way to achieve this is to separate the high level intention of the human in acting to achieve the goal from the precise low level movements of body and objects required to achieve that intention. In this paper we present a model containing just such a division. This research was supported by the EU FP6 IST Cognitive Systems Integrated Project Cognitive Systems for Cognitive Assistants “CoSy” FP6-004250-IP.

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Towards Affordance-Based Robot Control: International Seminar, Dagstuhl Castle, Germany, June 5-9, 2006. Revised Papers by Verena V. Hafner, Frédéric Kaplan (auth.), Erich Rome, Joachim Hertzberg, Georg Dorffner (eds.)

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