This experiment investigates a hyper-flexible cells scenario. In particular, we consider the following setting: A robot delivers parts to a process station. The parts are made available to the workspace of the robot in transport boxes that are not required to be packed in a systematic way. With its onboard 3D laser scanning sensor, the robot recognizes and identifies the topmost objects in the boxes. It grasps the parts out of the box and moves them to the station for further processing. The parts can be shaped nearly arbitrarily. We only assume that parts can be approximated by geometric shape primitives like e.g. cylinders or spheres. A new part type can be learned in a matter of minutes simply by presenting an exemplar to the robot.
The major challenges arising in this setting which will be addressed in the experiment are:
DFG Research Group "Learning Humanoid Robots"
funded within the Emmy Noether Programme
The cognitive service robots Dynamaid and Cosero have been developed for tasks in domestic environments.
They won the RoboCup 2011 and 2012 competitions.
Overview of projects in the ARC lab.
Selection of projects relative to mobile manipulation (as of End 2012):
Autonomous Servicing of On-Orbit Space Systems from Robotic Systems (NSRF)
Identification and Assessment of Existing Terrestrial Micro-systems and Micro-technologies for Space Robotics (ESA)
Development of a Flexible and Reliable Automated Warehouse System
Modeling and Control of Microrobotic Systems
MiCRoN: MIniature Co-operative RObots advancing towards the Nano-range
Autonomous Inspection of Subsea Telecommunication Cables, Power Cables and Pipelines (EU)
We are developing a grasp testbed for studying the process of grasp acquisition in human and robotic systems. The system is built around our Barrett WAM/Hand system on which we have implemented a hand-frame impedance controller, a Natural Point body tracking system, and multibody dynamic simulation capabiltiy. It will be further enhanced with the results of a recent NRI grant focused on Bayesian filtering for grasp perception.
Projects pertaining to mobile manipulation
Currently funded projects:
Human-Robot Interaction for Assistive Applications (HRIAA), funded by ANR Chair d'Excellence Control Software Achitecture for Robust Robot Manipulation (Carroman), funded by ADT INRIA
The main goal of the project BesMan is the development of one- and two-arm manipulation procedures as well as the learning of new situation-specific behaviors by means of a machine learning platform.
The goal of the research project IMPERA is the development of strategies for distributed mission and task planning. An application example is the exploration of unknown, lunar environment using a team of mobile robots