GeRT - Generalizing Robot Manipulation Tasks

In order to work naturally in human environments such as offices and homes, robots of the future will need to be much more flexible and robust in the face of novelty than those of today. Currently the most advanced robots can perform a task such as making a drink, which involves grasping, pouring, and twisting off a cap from a jar. But the rules for how to pick up every single object must be programmed. So the ability to manipulate fifty different objects means writing fifty different programs. Even worse than this is the fact that if one object in a task changes then the program for the whole task may need to be rewritten.

In GeRT we will develop new methods to cope with novelty in manipulation tasks. Our approach is to take a small set of existing robot programs, for a certain robot manipulation task, such as serving a drink and to give the robot the ability to adapt them to a novel version of the task. These programs constitute a database of prototypes representing that class of task. When confronted with a novel instance of the same task the robot needs to establishing appropriate correspondences between objects and actions in the prototypes and their counterparts in the novel scenario. In this way the robot can solve a task that is physically substantially different but similar at an abstract level. The project´s results will be demonstrated on the DLR platform Justin.

The main role of the AASS Cognitive Robotic Systems Lab is to develop hybrid planning techniques able to: (1) generalising the high-level behaviour in the robot programs, and (2) generating new high-level behaviours for novel but analog tasks.

GeRT is a EU FP7 STREP project. The overall budget is about 3,700,000 EUR. The share of Orebro University is about 670,000 EUR.