Cognitive Robotics

Cognitive Robotics

Hybris-C1: Planning and Action Control under Uncertainty for Mobile Manipulation Tasks

Mobile robots that are deployed, for instance, for domestic tasks, need to fulfill them even if they do not have complete information about the domain beforehand. Consider, for example, the task of fetching an object from a specific room. Consider further that the robot has a map of the environment and that it can plan a path to the living room. However, only when reaching the door of the living room, it will know whether or not the door is open. Moreover, it might not be known where in the living room the object is located such that the robot needs to make a plan how to find it. Next, a plan for grabbing the object in a safe way is required. This includes taking camera shots from different angles and finding an appropriate location to grab the object. Basically, the action control of the robot continuously needs to combine active perception and high-level planning with action execution. The robot has to deal with qualitative action and world descriptions as well as with uncertainty and quantitative data from sensors and actuators. The objective of this project is to develop methods for solving tasks like the above in an intelligent way by combining perception, high-level planning and action execution. An evaluation of the developed methods will be conducted on existing mobile robotic platforms.


Submitted by Jens Claßen on 1. February 2013 - 19:00 categories [ ]


Biological algorithms of sound localization may be useful in studying acoustic orientation of robots. One of us (Lakemeyer) collaborated in the software design of a mobile robot that can give guided tours through exhibitions, among other things. We now want to equip this robot with a sound localization and a speech recognition system. The Jeffress model of binaural interaction contains delay lines and coincidence detection. It is realized in the barn owl. We have implemented a Jeffress-like model on a computer and have tested it with noise and speech signals (Calmes et al., in preparation). This model works pretty well even in cluttered surroundings. Currently we are implementing the software on the robot.

Homepage: HeRBiE @ Bio II

Submitted by stf on 9. December 2007 - 15:10 categories [ ]

Lab Course: Goal Reasoning for Autonomous Mobile Robots in Logistics Scenarios

Lab course to develop a task-level coordination component based on goal reasoning for a competitive factory automation scenario in simulation.
Submitted by tim on 20. June 2016 - 10:34 categories [ ]

Lab Course: Distributed Task Planning for a Group of Mobile Robots WS 2015/2016

Lab course to develop a distributed planning component for a competitive factory automation scenario in simulation.

Submitted by tim on 16. June 2015 - 16:51 categories [ ]

Lab Course "Procedural Reasoning on a Group of Adaptive Mobile Robots" WS 2014/2015

Lab course to develop an software agent based on Procedural Reasoning Systems for a competitive factory automation in simulation.

Submitted by tim on 25. June 2014 - 18:58 categories [ ]

Seminar Dynamics of Knowledge and Belief SS 2014

In this seminar we will study several modeling and reasoning techniques for knowledge and belief in dynamic systems. Knowledge is an important aspect of intelligent programs: while most of today's systems assume a closed world, i.e., everything they don't know to be true is assumed to be false, an intelligent system needs to consider possible that there are truths not known to the system. In a dynamic environment, i.e., an environment where one or multiple agents (inter)act, the system will usually have to acquire new knowledge through sensing. Potentially it may even revise its beliefs when it realizes some beliefs were wrong. In this seminar we will study various aspects of action, knowledge, and belief.

We plan to implement a peer review process for this seminar. That is, every student will read some other students' term paper and provide feedback in form of a written review. This shall not only deepen your understanding of the other topics, but it also introduces you to the academic review process.

Submitted by Christoph Schwering on 6. January 2014 - 0:08 categories [ ]

Robotino Bonding Hackathon 2013

Together with Bonding, the IMA/ZLW & IFU Institute Cluster, RWTH Aachen University and the Department for Electrical Engineering and Information Technology, Robotics Group, FH Aachen we will host the Robotino Bonding Hackathon. Students will compete in small teams to develop the behavior of a robot operating in a simplified desaster scenario to develop effective behavior strategies to recover and prioritize items from the arena.

The event is organized by the Carologistics RoboCup Team.

Submitted by tim on 12. November 2013 - 14:40 categories [ ]

Lab Course "Robot System Management" WS 2013/2014

An announcement of the course can eventually be found in the course information system CAMPUS.

Submitted by tim on 9. July 2013 - 19:06 categories [ ]

Robotino Hackathon 2013

Together with the IMA/ZLW & IFU Institute Cluster, RWTH Aachen University and the Department for Electrical Engineering and Information Technology, Robotics Group, FH Aachen we founded a new joint team to participate in the RoboCup Logistics League sponsored by Festo (LLSF)

As a preparation for RoboCup 2013 in Eindhoven, there will be a Hackathon from February 25th to March 1st. The goal of this Hackathon is to develop high-level programs for reasoning and behavior execution for the new tasks and challenges in the LLSF. integrate a robotics system based on the Robotino robot, that can complete certain logistics task in a restricted environment.

Submitted by tim on 25. January 2013 - 16:10 categories [ ]

Seminar Plan and Activity Recognition SS 2013

In this seminar we will study several different approaches to and aspects of plan and activity recognition. Recognizing what other agents are doing is an important aspect of intelligent systems. For example, a domestic service robot needs to understand what the human is doing in order to interact with him in a reasonable way. And a self-driving car should know what the other traffic participants are doing right now and infer what they are going to do in the next moments. Different domains bring along different problems and needs for levels of expressiveness like partial observability, incomplete knowledge, non-deterministic actions, adversarial agents, potentially hazardous situations. We will study some of the latest research on these problems and work out the particular strengths and weaknesses.

The topics include recent papers on plan and activity recognition.

Submitted by Christoph Schwering on 11. January 2013 - 15:14 categories [ ]