ICRA 2012 Paper Abstract


Paper TuB310.5

Ruehl, Steffen Wilhelm (Research Center for Information Technology (FZI)), Xue, Zhixing (FZI), Dillmann, Rüdiger (KIT Karlsruhe Institute for Technology)

Monitoring of Manipulation Activities for a Service Robot Using Supervised Learning

Scheduled for presentation during the Interactive Session "Interactive Session TuB-3" (TuB310), Tuesday, May 15, 2012, 11:30−12:00, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on August 21, 2018

Keywords Service Robots, Learning and Adaptive Systems, Grasping


To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions.

The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the sucessful state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the successful or failure state from the the high dimensional data collection.

We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulted state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.



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