By Rainer Jäkel, Steffen W. Rühl, Sven R. Schmidt-Rohr (auth.), Bruno Siciliano (eds.)
Dexterous and self reliant manipulation is a key know-how for the non-public and repair robots of the long run. Advances in Bimanual Manipulation edited through Bruno Siciliano presents the robotics neighborhood with the main visible result of the four-year eu venture DEXMART (DEXterous and self reliant dual-arm hand robot manipulation with clever sensory-motor talents: A bridge from usual to man made cognition). the amount covers a bunch of hugely very important issues within the box, fascinated by modelling and studying of human manipulation abilities, algorithms for activity making plans, human-robot interplay, and greedy, in addition to layout of dexterous anthropomorphic palms. the consequences defined during this five-chapter assortment are believed to pave the way in which in the direction of the improvement of robot platforms endowed with dexterous and human-aware dual-arm/hand manipulation talents for gadgets, working with a excessive measure of autonomy in unstructured real-world environments.
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Additional resources for Advanced Bimanual Manipulation: Results from the DEXMART Project
The goal is to place the tin in the center of the table, which is blocked by the box. The pick-and-place action to archive the goal requires the target spot to be free, therefor a second pick-and-place is planed to fulfill this condition. The action model used for planning is divided into three abstraction layers. A fragment is visualized in the example in Fig. 29. The highest level represents the course of actions in the scene, containing objects, locations, symbolic properties such as on/off and mechanical relations, as described in Sect.
Nonetheless, each layer performs different kinds of planning on these situation models. Manipulation strategy execution utilizes geometric models directly for collision-free constraint based motion planning. Scene driven reasoning performs 38 R. J¨akel et al. Fig. 33 The execution-time three-layer architecture. moderate abstraction on the geometric representation to derive predicates usable for logic-based planning. In turn, being on the highest level of abstraction and integrating distinct skill domains, the grade of abstraction is highest for mission-level action selection.
On anthropomorphic and humanoid service robots, tight integration between layers is achieved by sharing many properties of models and environment information . POMDPs are an action planning framework for autonomous agents considering uncertainty both in measurements of the current state of the world as well as predicted results of actions . Employing an action selection policy, reflecting these uncertainties together with quantified mission objectives (rewards), an agent can be able to assess risks and opportunities to optimize long-term success .