By Rainer Jäkel, Steffen W. Rühl, Sven R. Schmidt-Rohr (auth.), Bruno Siciliano (eds.)
Dexterous and self sufficient manipulation is a key expertise for the non-public and repair robots of the longer term. Advances in Bimanual Manipulation edited by way of Bruno Siciliano offers the robotics group with the main obvious result of the four-year ecu undertaking DEXMART (DEXterous and self sufficient dual-arm hand robot manipulation with clever sensory-motor abilities: A bridge from common to man made cognition). the amount covers a number of hugely very important themes within the box, curious about modelling and studying of human manipulation talents, algorithms for job making plans, human-robot interplay, and greedy, in addition to layout of dexterous anthropomorphic palms. the implications defined during this five-chapter assortment are believed to pave the way in which in the direction of the advance of robot structures endowed with dexterous and human-aware dual-arm/hand manipulation abilities for items, 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 orientation constraints restricts, that the cap is rotated between 0◦ and 45◦. The corresponding node constraints, which represents the goal Layered PbD and Planning for Autonomous Robot Manipulation 21 Fig. 21 Execution of planning result for opening a bottle on Adero. Fig. 22 Execution of planning result for lifting and opening a bottle on Adero. orientation of the cap, restricts the cap to be 40◦ rotated. The resulting manipulation strategy was executed using constrained motion planning with dynamics simulation, see Fig.
In order to generate a sequence of actions leading to a desired goal state, a logic-based planner is employed. Queries to a classic manipulation planner ensure the feasibility of single plan steps, meaning that there is a strategy for the robot that implements the symbolic action. The introduction of logic-based planning introduces a new challenge: the complex continuous scene in which the robot operates has to be mapped to a small but sufficiently descriptive set of symbols on which the planning operates.
Segmentation is provided by the logic-based planner, failure cases are generated by the experimenter manually removing the object either form the table or from the gripper. In the first experiment, a single object is used to train the svm. As seen in the table, this is insufficient for correct classification (45% accuracy on test data). Training on 5 objects leads to a better performance of 93% percent correctly classified data points. This experiment still includes one untrained object. The training time takes about 10 minutes per object, where the user has to supervise the robot.