By Rainer Jäkel, Steffen W. Rühl, Sven R. Schmidt-Rohr (auth.), Bruno Siciliano (eds.)
Dexterous and self reliant manipulation is a key expertise for the private and repair robots of the long run. Advances in Bimanual Manipulation edited via Bruno Siciliano presents the robotics group with the main obvious result of the four-year eu venture DEXMART (DEXterous and self sufficient dual-arm hand robot manipulation with clever sensory-motor talents: A bridge from average to synthetic cognition). the amount covers a number of hugely vital themes within the box, involved in modelling and studying of human manipulation talents, algorithms for activity making plans, human-robot interplay, and greedy, in addition to layout of dexterous anthropomorphic arms. the consequences defined during this five-chapter assortment are believed to pave the best way in the direction of the improvement of robot platforms endowed with dexterous and human-aware dual-arm/hand manipulation abilities for gadgets, working with a excessive measure of autonomy in unstructured real-world environments.
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Additional info for Advanced Bimanual Manipulation: Results from the DEXMART Project
To compensate it, we run the simulation until all objects rests without motion before the tests are executed (rest phase). For this approach to be feasible, the initial scene has to be static. Anyhow, we have made this assumption on the scene for it to be able to manipulate in it, so for the scene relation generation, it introduces no new restrictions. The dynamic tests are run multiple times with different randomly Layered PbD and Planning for Autonomous Robot Manipulation (a) (b) 29 (c) Fig. 26 Dynamic scene relations.
Considering uncertainty is feasible . DL-ontology based reasoning can easily be modularized and made portable over different robot architectures and planning paradigms . Scalability to robotic domains beyond service robots has also been investigated , . PbD of symbolic, abstract task and mission representations (in contrast to trajectory level representations discussed in Sect. 2) has been investigated for various task sequencing and planning methodologies. Symbolic Hierarchical Task Networks (HTNs) can be learned from segmentation of human demonstrations of manipulation tasks .
Instead, as with manipulation strategies, recording and analysis of human mission demonstrations is performed, which, in combination with some explorative learning refinement steps, generates a full mission model. In summary, strategic mission level decision making has to provide symbolic action selection while considering situative uncertainty and covering multiple, distinct skills domains. Mission model generation is made feasible utilizing PbD. 1 Related Work Information processing architectures for autonomous service robots have been investigated in detail.
Advanced Bimanual Manipulation: Results from the DEXMART Project by Rainer Jäkel, Steffen W. Rühl, Sven R. Schmidt-Rohr (auth.), Bruno Siciliano (eds.)