首页|Findings from Mondragon Unibertsitatea Yields New Data on Robotics (Learning Per iodic Skills for Robotic Manipulation: Insights On Orientation and Impedance)

Findings from Mondragon Unibertsitatea Yields New Data on Robotics (Learning Per iodic Skills for Robotic Manipulation: Insights On Orientation and Impedance)

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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news originating from Arrasate Mondragon, Spain, by NewsRx correspondents, research stated, "Many daily tasks exhibit a periodic na ture, necessitating that robots possess the ability to execute them either alone or in collaboration with humans. A widely used approach to encode and learn suc h periodic patterns from human demonstrations is through periodic Dynamic Moveme nt Primitives (DMPs)." Funders for this research include Basque Government, European Union (EU), Vinnov a. Our news journalists obtained a quote from the research from Mondragon Unibertsi tatea, "Periodic DMPs encode cyclic data independently across multiple dimension s of multi-degree of freedom systems. This method is effective for simple data, like Cartesian or joint position trajectories. However, it cannot account for va rious geometric constraints imposed by more complex data, such as orientation an d stiffness. To bridge this gap, we propose a novel periodic DMP formulation tha t enables the encoding of periodic orientation trajectories and varying stiffnes s matrices while considering their geometric constraints. Our geometry-aware app roach exploits the properties of the Riemannian manifold and Lie group to direct ly encode such periodic data while respecting its inherent geometric constraints . We initially employed simulation to validate the technical aspects of the prop osed method thoroughly."

Arrasate MondragonSpainEuropeEmerg ing TechnologiesMachine LearningNano-robotRoboticsRobotsMondragon Unib ertsitatea

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.3)