Catching objects in flight

Past Project Grants

1 February 2015 – 31 May 2019

Compliant control in humans is exploited in a variety of sophisticated skills. These include solitary actions such as soft catching, sliding, pushing large objects as well as joint actions performed in teams such as mainpulation of large scale objects or mutual adaptation through phyiscal coupling for learning, in walking or in execution of joint tasks. This advanced ability of organizing versatile motion is refer under varying contact and impedance as cognitive compliant interaction in motion. The COgIMon project aims at a step-change in human-robot interaction toward the systemic integration of robust, dependable interaction capabilities for teams of humans and compliant robots, in particular the compliant humanoid COMAN.

RoboHow

1 February 2012 – 31 July 2016

In order to make robots achieve robust, adaptive, effective and natural performance of everyday manipulation tasks, it is not feasible to expect that programmers can equip the robots with plan libraries that cover such open-ended task spectrum competently. ROBOHOW targets at enabling autonomous robots to perform expanding sets of human-scale tasks – both in human working and living environments. To this end, ROBOHOW will investigate a new approach to robot programming and control where knowledge for accomplishing tasks is semi-automatically acquired from instructions in the World Wide Web, from human instruction and from demonstration.

AMARSi

1 March 2010 – 28 February 2014

The AMARSi Integrated Project aims at a qualitative jump toward biological richness of robotic motor skills.

Acquiring rich motor skills will change the role of robots in our human’s society in two fundamental ways. First, such robots will be much more versatile than today, with greatly expanded ranges of practical usages. And second, the naturalness and compliance of their motor behavior will make them blend into the everyday routines of human society, physically safe and psychologically acceptable.

First-MM

1 February 2010 – 31 July 2013

Many industrial processes highly depend on the reliability and robustness of robotic manipulators. Research on mobile robots has led to systems that demonstrated the capability of safe and accurate navigation. The goal of the FIRST-MM project is to integrate these two areas in the context of a real-world application scenario. The project will build upon and extend recent results in robot programming, navigation, manipulation, perception, learning by instruction, and statistical relational learning to develop advanced technology for autonomous mobile manipulation robots that can flexibly be instructed even by non-expert users to perform challenging manipulation and transportation tasks in real-world environments