The TidyUpRobot Project
Wolfram Burgard, Bernhard Nebel, Martin Riedmiller, Jürgen Sturm, Felix Endres, Jürgen Hess, Bastian Steder, Christian Dornhege, Sascha Lange, Stefan Welker
Department of Computer Science University of Freiburg, Germany University of Freiburg, Germany
Freiburg has 200.000 inhabitants Freiburg University has 30.000 students Traditional university with a technical faculty (CS + MST) Strong in AI and robotics Robotics and AI in Freiburg Autonomous Intelligent Systems Wolfram Burgard
Foundations of Artificial Intelligence Bernhard Nebel
Machine Learning Martin Riedmiller
Humanoid Robots Maren Bennewitz Social Robotics Kai Arras Autonomous Intelligent Systems
Mobile robots Mapping Localization Navigation Probabilistic robotics Multi robot systems Embedded systems Foundations of Artificial Intelligence
Action planning Theory and practice Planning competitions Qualitative temporal spatial reasoning RoboCup soccer World champion three times Autonomous table soccer Robocup Rescue League Machine Learning
Reinforcement learning Neural networks Fast, efficient learning Neural controllers Applications: Forecasting systems RoboCup soccer Industry Project Goal of TidyUpRobot
Reliable execution of fetch and carry tasks Mobile manipulation Domestic environments Example: Clear table in the living room, place objects in cabinets where they belong Module Overview
Re useable software modules Licensed under (L)GPL
Feature-based Symbolic Planner With Object Recognition Semantic Attachments
Generalized Map Representation
Kinematic Models for Reinforcement Learning Articulated Objects Motion Controller Module: Object Recognition
Learn object models Detect and localize objects in point clouds Module: Object Recognition Approach: Match point features from models Estimate object candidate pose Score and filter candidates Module: Articulated Objects
Detect articulated objects Learn kinematic models Open doors and drawers Use cupboards to stow objects Module: Generalized Mapping
Topological maps (multi floor) Augmented with semantic information Detected (articulated) objects Kinematic models Action/skill models Symbolic description (for planner) Module: Semantic Planning
Plan with incomplete knowledge Sensory actions that gain knowledge Assertions (pre /post conditions) Continuous re planning, triggered by assertions Module: Semantic Planning
Problem: Not all real world facts can be represented efficiently on the logical level Solution: Semantic attachments (use the specific algorithms, e.g., geometric path planner) Module: Action/Skill Learning
Learning from experience: Reinforcement Learning for optimizing initial trajectories Improve available actions Acquire and provide new actions Identify constraints to be integrated as new semantic attachments Project Evaluation
Task execution reliability (>90%) Repeatability at three other PR2 sites (≥66%) Re usability of our code (three other groups, using at least one module) Open-Source Contributions
Graph based map representation (LGPL) 3D object detection and localization (LGPL) Articulated Objects (LGPL) Symbolic planner (GPL) Manipulation skill learner (LGPL)
http://code.google.com/p/alufr-ros-pkg Conclusions
Joint initiative of the AI and robotics groups at the University of Freiburg TidyUpRobot Project Reliable execution of fetch and carry tasks Looking for labs interested in using our modules/application