AI News, An algorithm to teach robots pre

An algorithm to teach robots pre-grasping manipulation strategies

In recent years, several researchers have tried to reproduce human manipulation strategies in robots, yet fewer studies have focused on pre-grasping manipulation.

The algorithm developed by Berscheid and his colleagues takes the robot's training one step further, allowing it to also acquire useful for pre-grasping manipulation strategies, such as shifting or pushing.

Training robots in reality is very tricky: First, it takes a long time, so the training itself needs to be automated and self-supervised, and second a lot of unexpected things can happen if the robot explores its environment.

In other words, our work is connected to two very challenging research questions: How can a robot learn as fast as possible—and what tasks can a robot learn using the discovered insights?'

As Berscheid goes on to explain, a robot can learn more efficiently if it receives direct feedback after each action it performs, as this overcomes the issue of sparse rewards.

The approach proposed by the researchers is based on a previous study that investigated the use of differences in grasping probabilities before and after a particular action, focusing on a small area around where the action is performed.

The algorithm presented by the researchers allows a robot to learn the optimal pose for pre-grasping actions such as clamping or shifting, as well as how to perform these actions to increase the probability of successful grasping.

The researchers applied their algorithm to a Franka robotic arm and then evaluated its performance on a task that involves picking up objects from a bin until it is completely empty.

Second and of more practical relevance, this could be very useful in the automation of many industrial tasks, particularly for bin picking, where the robot should be able to empty the bin completely on its own.'

According to Berscheid, the main challenge when trying to achieve this will be ensuring that the robot acquires lateral grasps while keeping the number of grasp attempts it performs constant during the training phase.

Professur für Lehr-Lernforschung Prof. Dr. Ines Langemeyer

Sie war seit 2001 als wissenschaftliche Mitarbeiterin im Arbeitsbereich Medienforschung (Freie Universität Berlin), am Seminar für Medien und Kommunikation (Universität Erfurt), am Lehrstuhl für Wirtschafts- und Industriesoziologie (BTU Cottbus) sowie als Postdoctoral Research Fellow im Forschungsbereich InterMedia (University of Oslo) tätig.

2012-2013 hatte sie die Vertretungsprofessur für Lehr-Lernforschung am KIT inne, bis sie 2013 dem Ruf auf die Professur für Erwachsenenbildung/Weiterbildung der Universität Tübingen folgte.

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