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Humanoid Robots Lab

Humanoid Robots Lab. BA-INF 051 Projektgruppe MA-INF 4213 Seminar MA-INF 4214 Lab. Prof. Dr. Maren Bennewitz. Supervisors: Dr. Marcell Missura Arindam Roychoudhury Peter Regier Lilli Bruckschen. Courses. Seminar : Presentation and discussion of relevant scientific work

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Humanoid Robots Lab

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  1. Humanoid Robots Lab BA-INF 051ProjektgruppeMA-INF 4213 SeminarMA-INF 4214 Lab Prof. Dr. Maren Bennewitz Supervisors: Dr. Marcell Missura Arindam Roychoudhury Peter Regier Lilli Bruckschen

  2. Courses • Seminar: Presentation and discussionof relevant scientific work • Lab: Programming project with a humanoid robot • Project Group: Lab (2/3) + Seminar (1/3)

  3. MA-INF 4213 Seminar

  4. Seminar Overview • Presentation and discussion of relevant scientific work (conference/journal papers) • What is the new contribution of the work? How does the technique work? What are the strengths and the weaknesses of the approach? • MSc students: Summary and discussion of the work (7 pages not counting figures, LaTeX template provided on web page)

  5. Seminar Overview • Prepare during the semester (at home) • Understand the paper • Write summary (MSc) • Prepare your presentation • Seminar Day at the end of the semester • Everybody has to present • Everybody has to be present • It’s a full day event!

  6. Seminar Grade BSc Students: • Presentation: 100% MSc Students: • Presentation: 70% • Summary and discussion: 30%

  7. Seminar Papers

  8. Learning without ForgettingLi, Zhizhong, and Derek Hoiem IEEE Transactions on Pattern Analysis and Machine Intelligence 40.12 (2018). Supervisor: Lilli Bruckschen • Goal: improve CNN transfer learning • Methods: train a network while preserving the original capabilities

  9. DeepPicar: A Low-cost Deep Neural Network-based Autonomous CarBechtel, Michael G., et al.RTCSA 2018. Supervisor: Lilli Bruckschen • Goal: create a low-cost deep neural network based autonomous car platform • Methods: use a CNN, which takes images from a front-facing camera as input and produces car steering angles as output

  10. From Perception to Decision: A Data-Driven Approach to End-to-End Motion Planning for Autonomous Ground Robots Mark Pfeiffer, Michael Schaeuble, Roland Siegwart and Cesar Cadena ICRA 2017, Supervisor: Marcell Missura • Neural Network-based learning of steering commands for a wheeled robot • Supervised learning with the help of a ROS path planner

  11. Fast Kinodynamic Bipedal Locomotion Planning with Moving Obstacles J. Ahn, O. Campbell, D. Kim, and L. Sentis IROS 2018, Supervisor: Marcell Missura • Sampling-based footstep planning framework • Idea: handle footstep planning and biped dynamics simultaneously • Challenge: achieve short planning time while considering collisions with the environment

  12. Robot Companion: A Social-Force based approach with Human Awareness-Navigation in Crowded EnvironmentsGonzalo Ferrer, Anais Garrell, Alberto SanfeliuIROS 2013, Supervisor: Peter Regier • Introducing the companion force to the SFM • Human feedback based model learning and evaluation • Goal: Accompany a pedestrian to its destination in an urban environment

  13. Map-based Deep Imitation Learning for Obstacle AvoidanceYuejiang Liu, An Xu and Zichong Chen IROS 2018, Supervisor: Peter Regier • Learning local policy based on an egocentric local occupancy map • Artificial data generation for real-world robot application • Idea: Formulate the obstacle avoidance for mobile robots as an imitation learning problem

  14. Design of an Autonomous Racecar: Perception, State Estimation and System IntegrationMiguel I. Valls, Hubertus et al. ICRA 2018, Superviser: Arindam Roychoudhury • SLAM • Complete pipeline from perception to state estimation • driving an autonomous race car close to a human driver’s performance.

  15. UAV-Based Crop and Weed Classification for Smart FarmingPhilipp Lottes et al. ICRA 2017, Superviser: Arindam Roychoudhury • Use UAVs (quad-copter) for knowledge of type and distribution of weeds in the field. • RGB and NIR camera. • Vision based weed classification.

  16. Real-Time Path Planning in Unknown Environments for Bipedal RobotsHildebrandt et al., RAS-L, 2018, Supervisor: Maren Bennewitz • Autonomous navigation in dynamic and unknown environments with humanoids • Search for multiple 2D paths to exploit the capacities of bipedal walking in cluttered environments • Combine 2D path planning with calculation of footstep positions • Leads to fast reactivity in dynamically changing environments

  17. A Single-Planner Approach to Multi-Modal Humanoid MobilityDornbush et al., ICRA, 2018, Supervisor: Maren Bennewitz • Plan for humanoid mobility, consider locomotion tasks such as bipedal walking, crawling, and climbing • Plan for all these tasks within a single search process • Allows the search to reason about all capabilities at any point and to derive a complete solution • Decompose the mobility task into a sequence of smaller tasks and reason over smaller search spaces

  18. MA-INF 4214 Lab

  19. Programming Projects • Small groups of 2-3 people • Work with the Nao humanoids (in the lab) • Individual projects involving perception and action generation • Presentation and written documentation at the end of the semester

  20. Lab Grade • Active participation, performance of the system: 85% • Presentation: 15% • Individual grade for each group member • Satisfying documentation is a precondition!

  21. Lab Projects

  22. Soccer • Score a penalty kick • Detect goal and ball, walk up to the ball, and kick the ball into the goal

  23. Roll the Dice • Play a dice game with Nao • Roll the die, read the points, play according to rules, interact with human

  24. Follow the Path • Follow a path marked by yarn, evade obstacles and bridge gaps in the path

  25. Toy Detection • Train a Convolutional Neural Network to detect a toy and retrieve it with the robot

  26. Picar • Some of the projects can also be done with a (experimental) picar

  27. Lab Rules

  28. 1. Rule • The lab closes at 6pm. If you are the last to leave the lab, please ask one of the supervisors to lock up.

  29. 2. Rule • To open or to close the windows, please ask one of the supervisors.

  30. 3. Rule • When you are not using the Nao, please set it into the rest mode by double pressing the button on the chest.

  31. 4. Rule • Please use the harness during walking!

  32. 5. Rule • Clean up after yourself.

  33. Registration

  34. Next Steps • Two separate registrations are necessary! • Registration on our web site (first-come-first-serve!) until Friday, April 19nd • Topic and group assignment:Monday afternoon (notification via e-mail) • Registration in BASIS until Tuesday, April 30th

  35. Apply for Exam in BASIS

  36. Schedule

  37. Fragen? Questions?

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