Explore Labs

Object Transportation Lab

Explore Labs

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Description

This laboratory is dedicated to advancing the capabilities of robot agents in seamlessly executing object transportation tasks within human-centric environments such as homes and retail spaces. It provides a versatile platform for exploring and refining generalized robot plans that manage the movement of diverse objects across varied settings for multiple purposes. By focusing on the adaptability and scalability of robotic programming, the lab aims to enhance the understanding and application of robotics in everyday contexts ultimately improving their generalizability, transferability, and effectiveness in real-world scenarios.

In the laboratory, you are equipped with a generalized open-source robotic plan capable of executing various object transportation-related tasks, including both table setting and cleaning, across diverse domestic settings. These settings range from entire apartments to kitchen environments and the plan is adaptable to various robots. You can customize the execution by selecting the appropriate environment, task, and robot, and then run it within a software container.

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Every time we think that we are getting a little bit closer to a household robot, new research comes out showing just how far we have to go. Certainly, we have seen a lot of progress in specific areas like grasping and semantic understanding etc., but putting it all together into a hardware platform that can actually do things autonomously still seems to be a long way to go.

In a paper presented at ICRA 2021, researchers from the University of Bremen conducted a “Robot Household Marathon Experiment,” where a PR2 robot was tasked with first setting a table for a simple breakfast and then cleaning up afterwards in order to “investigate and evaluate the scalability and the robustness aspects of mobile manipulation.” While this may seem like something robots should have figured out, you might not be surprised to learn that it is actually still a significant challenge.

Example Videos

Software Components

  • CRAM: A software toolbox for implementing autonomous robots. Source Code
  • KnowRob: A knowledge processing system for robots. Source Code
  • OpenEASE: A web-based knowledge service providing robot and human activity data. Source Code
  • GISKARD: A framework for constraint- and optimization-based robot motion and planning control. Source Code
  • ROBOKUDO: A perception framework targeted for robot manipulation tasks. Source Code
  • PyCRAM: The Python 3 re-implementation of CRAM, serving as a toolbox for designing, implementing, and deploying software on autonomous robots. Source Code

Courses and Tutorials

  • Integrated Intelligent Systems: Covers contemporary AI techniques in cognitive robotics. Course Link
  • Robot Programming with ROS: An introduction to the Robot Operating System (ROS). Course Link
  • SUTURO - sudo tidy-up-my-room: A project where students design their own applications to run on real robots. Course Link

For more information on these courses, visit the University of Bremen's AI department page.

Authors and Contact Details

Publications

  • The Robot Household Marathon Experiment by Gayane Kazhoyan, Simon Stelter, Franklin Kenghagho Kenfack, Sebastian Koralewski, and Michael Beetz. Presented at the IEEE International Conference on Robotics and Automation (ICRA), 2021. DOI: 10.48550/arXiv.2011.09792
  • Towards Plan Transformations for Real-World Mobile Fetch and Place by Gayane Kazhoyan, Arthur Niedzwiecki, and Michael Beetz. Presented at the IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 11011-11017. DOI: 10.1109/ICRA40945.2020.9197446
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