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Multi Robot OITL Scaling Experiments

Multi Robot OITL Scaling Experiments. Research Team: U of Pittsburgh: M. Lewis Huadong Wang, Shih Yi Chien, Zheng Ma, Peiju Lee, Dhruba Baishya CMU: K. Sycara, P. Scerri Prasanna Velagapudi, Breelyn Kane. Cornell. MIT. GMU. Pitt. CMU Robotics. CMU Psychology. Scaling of cognitive

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Multi Robot OITL Scaling Experiments

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  1. Multi Robot OITL Scaling Experiments Research Team: U of Pittsburgh: M. Lewis Huadong Wang, Shih Yi Chien, Zheng Ma, Peiju Lee, DhrubaBaishya CMU: K. Sycara, P. Scerri Prasanna Velagapudi, Breelyn Kane MURI Review

  2. Cornell MIT GMU Pitt CMU Robotics CMU Psychology Scaling of cognitive performance and workload Level 1,3 Level 1,2 Level 1 Level 2 Level 1-2.5 Level 1-3 Level 1 Level 1,3 Task allocation among humans/agents Probabilistic models of human decision-making in network situations Level 1,2 Level 1-2.5 Level 3 ? Level 2 Level 1-3 Decentralized control search and planning Level 1,2 Level 2 Information fusion Level 1,2 Level 1,3, 4 Network performance as a function of topology Level 4 Level 2 Communication, evolution, language Level 3 Level 2, 3 Adaptive automation Level 1,2 Level 1

  3. Architectural Framework for Human Control of Multirobot Teams For controlling large(r) teams we need to consider how difficulty for the operator grows in N robots Borrowing from computational complexity we think there are 3 basic classes of commands O(1) difficulty independent of N robots O(n) difficulty proportional to N robots O(>n) increase much greater than additive Vision: Collaborating teams of humans & robots using “commands” from all classes First Year Review

  4. Setting Goals: O(1) Operator draws regions to be searched on screen The complexity of the plan is independent of N of robots Robots may be either independently autonomous or cooperating autonomously First Year Review

  5. Individual Control: O(n) Teleoperation or Waypoint control Each additional robot adds the same incremental effort First Year Review

  6. Coordinating robots O(>n) First Year Review

  7. As size grows, complexity of Coordination should dominate O(>n) O(n) Cognitive limit O(1) N of Robots First Year Review

  8. Plan of Attack • O(1)- many problems such as opacity but not scaling • O(>n)- Automate coordination • because difficulty is combinatorial and interdependence of action precludes human control • O(n) because of independence among robots amenable to scheduling, automation, and teamwork interventions • Neglect tolerance model • Call center & other team centered approaches • individual automation First Year Review

  9. Plan of Attack • O(1)- many problems such as opacity but not scaling • O(>n)- Automate coordination • because difficulty is combinatorial and interdependence of action precludes human control • O(n) because of independence among robots amenable to scheduling, automation, and teamwork interventions • Neglect tolerance model • Call center & other team centered approaches • individual automation First Year Review

  10. Problem: Human Control ofLarge(r) Robot Teams IDEA: Examine “difficulty” of control tasks as N robots increases Identify which tasks must be automated or allocated differently to allow control of larger teams Automate identified tasks & retest First Year Review

  11. Methodology – Experiment Design First Year Review • Fulltask condition: Participants both dictated the robots’ paths and controlled their cameras to search for victims and to mark them on the map. • Explorationsubtask: Participants directed the team of robots in order to explore as wide an area as possible. • Perceptual search condition: Participants searched for victims by controlling cameras mounted on robots following predetermined paths selected to match characteristics of paths generated under the other two conditions.

  12. USARSim Validation Studies • Synthetic video • Carpin, S., Stoyanov, T., Nevatia, Y., Lewis, M. and Wang, J. (2006a). Quantitative assessments of USARSim accuracy". Proceedings of PerMIS 2006 • Hokuyo laser range finder • Carpin, S., Wang, J., Lewis, M., Birk, A., and Jacoff, A. (2005). High fidelity tools for rescue robotics: Results and perspectives, Robocup 2005 Symposium. • Platform physics & behavior • Carpin, S., Lewis, M., Wang, J., Balakirsky, S. and Scrapper, C. (2006b). Bridging the gap between simulation and reality in urban search and rescue. Robocup 2006: Robot Soccer World Cup X, Springer, Lecture Notes in Artificial Intelligence • Lewis, M., Hughes, S., Wang, J., Koes, M. and Carpin, S., Validating USARsim for use in HRI research, Proceedings of the 49th Annual Meeting of the Human Factors and Ergonomics Society, Orlando, FL, 457-461, 2005. • Pepper, C., Balakirsky, S. and Scrapper, C., Robot Simulation Physics Validation, Proceedings of PerMIS’07, 2007. • Taylor, B., Balakirsky, S., Messina, E. and Quinn, R., Design and Validation of a Whegs Robot in USARSim, Proceedings of PerMIS’07. • Zaratti, M., Fratarcangeli, M., and Iocchi, L., A 3D Simulator of Multiple Legged Robots based on USARSim. Robocup 2006: Robot Soccer World Cup X, Springer, LNAI, 2006. www.sourceforge.net/project/usarsim First Year Review

  13. IntroductionGUI for Multi-robot Control First Year Review

  14. Robots and Maps Office-like environments P2AT Robot First Year Review

  15. Methodology – Experiment Design Number of Robots Conditions First Year Review Between Groups repeated measure design 3*15 Participants from University of Pittsburgh Standard Instruction, 20min Training, 3*15min Testing Session, NASA-TLX workload survey after each testing Session

  16. Methodology – Experiment Design First Year Review • Independent Variables: • Conditions of Task • Numbers of Robots • Dependent Variables: • NASA-TLX Workload • Victims found • Area Explored • Switches in focus among robots • Number of assigned missions • Average path length • Robots neglected or operated only once

  17. Results F1,28 = 27.4 p < .0001 First Year Review Victim Found as a function of N robots

  18. Results F1,28 = 21.17 p < .002 First Year Review Area explored as a function of N robots

  19. Results F1,27 = 21.17 p < .0001 Fulltask x search First Year Review Workload as a function of N robots

  20. Conclusions Experiment 1 • Exploration was the limiting component of Full task performance • η2 for improvement in performance with team size much higher for finding victims than area explored • Workload much lower for perceptual search • Full task performance “fell apart” at 12 • Half the victims found as in perceptual search First Year Review

  21. Call Center Control • Restricted to O(n) commands • Responsive to variation in demands • Performance depends on match between model & task • automation vs. monitoring First Year Review

  22. How a Call Center Works • As requests arrive operators service them using FIFO or similar discipline • Does not require monitoring or extended SA • Can benefit from scheduling results for assigning tasks based on expected duration • As operators’ task approaches that of a “server” control benefits will become more pronounced • Requires automating navigation through path planning, monitoring through self diagnosis/reflection, etc. First Year Review

  23. Team Experiment-1 (control) Assigned robots- each operator assigned 12 robots to control VS. Call Center- both operators given opportunity to control any of the 24 robots Task: foraging same as earlier fulltask condition Operator role poor approximation of “server” First Year Review

  24. 12 Assigned Robot Condition First Year Review

  25. 24 Robot Control Interface First Year Review

  26. Performance better for assigned robots First Year Review

  27. Call Center operators neglect more robots First Year Review

  28. Workload is higher for assigned robots First Year Review

  29. Team Experiment 2 Path planning automated using max entropy algorithms (previously used for UAVs) • Operators monitor to find victims and free stuck robots • Algorithm performance shown comparable to human operators (data from experiment-1) • New algorithms designed & tested for high traffic control Assigned robots- each operator assigned 12 robots to control VS. Call Center- both operators given opportunity to control any of the 24 robots Spatial Orientation test added to protocol to provide data for cognitive modeling of workload & attention switching First Year Review

  30. Experiment 2 uses data from 1 as control First Year Review

  31. Victims Found F1,56 =13.436 p=.001 difference First Year Review

  32. Region explored both main effects & interaction Autonomy F1,56=6.982 p=.011 Interaction F1,56=7.878 p=.007 Team F1,56=3.701 p=.059 difference difference First Year Review

  33. Victims/Area: effect for autonomy, interaction, & autonomy for assigned robots Autonomy F1,56=7.138 p=.01 Interaction F1,56=7.138 p=.054 First Year Review

  34. RMS Error Teams F1,56=18.031 p<.001 Autonomy F1,56=5.434 p<.023 First Year Review

  35. NASA-TLX Team t(118)=1.933 p=.056 First Year Review

  36. Collaborations • Working with Christian Lebiere & David Reitter (CMU) to develop cognitive models of operators Reitter, D., Lebiere, C., Lewis, M., Wang, H., and Ma, Z. (2009). A cognitive model of visual path planning in a multi-robot control system, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, October. • Supplying data for team supervisory control model being developed by Brian Mekdeci in Missy Cumming’s lab First Year Review

  37. Future Research Extend to operator teams for O(n) • Self diagnosis • Self reflection • Asynchronous/merged camera views • Replays for SA First Year Review

  38. New Areas • O(1)- many problems such as opacity but not scaling • O(>n)- Automate coordination • because difficulty is combinatorial and interdependence of action precludes human control • O(n) because of independence among robots amenable to scheduling, automation, and teamwork interventions • Neglect tolerance model • Call center & other team centered approaches • individual automation First Year Review

  39. Effective coordination for interdependent UVs Translucent • Plan libraries & explicit human roles • Machinetta MAS implemented in current testbed • OITL experiments to begin after Call Center completes Opaque • Centralized controller typically optimizing • Will investigate divide & conquer design approach • Biologically inspired control laws & emergent coordination • Will investigate amorphous algorithm approach First Year Review

  40. Things that make Algorithms Opaque • Centralized optimization requires defining a figure of merit (FM)/goal to guide execution • Human goals involve domain objects but algorithm only optimizes to its FM Operator lacks expressivity e.g. hit X Operator lacks mental model • Biologically inspired local control laws lack levers for human control • Investigate human intelligible propagating control approaches First Year Review

  41. Cornell MIT GMU Pitt CMU Robotics CMU Psychology Scaling of cognitive performance and workload Level 1,3 Level 1,2 Level 1 Level 2 Level 1-2.5 Level 1-3 Level 1 Level 2 Level 1,3 Task allocation among humans/agents Probabilistic models of human decision-making in network situations Level 1,2 Level 1-2.5 Level 3 ? Level 2 Level 1-3 Decentralized control search and planning Level 1,2 Level 2 Information fusion Level 1,2 Level 1,3, 4 Level 1,2 Network performance as a function of topology Level 4 Level 2 Communication, evolution, language Level 3 Level 2, 3 Adaptive automation Level 1,2 Level 1 Level 1,2

  42. Publications Wang, H., Lewis, M., Velagapudi, P., Scerri, P., and Sycara K. (2009). How Search and its Subtasks Scale in N Robots, Proceedings of the Forth ACM/IEEE International Conference on Human-Robot Interaction (HRI'09), March 9-13. Lewis, M., Wang, H., Velagapudi, P., Scerri, P. & Sycara, K. (2009). Using humans as sensors in robotic search, Proceedings of the 12th International Conference on Information Fusion, July 6-9. Baishya, D. &Lewis, M. (2009). Algorithm steering for mixed-initiative robot teams, 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’09) Workshop on Mixed-Initiative MAS, May 10-15. Lewis, M., Balakirsky, S. & Carpin, S. (2009). Contributions of the virtual robot RoboCup Rescue competition to research in robotics, 21st International Joint Conference on Artificial Intelligence (IJCAI’09) Workshop on Competitions in Artificial Intelligence and Robotics, July 12. Lewis, M., Sycara, K., & Scerri, P. (2009). Scaling up wide-area-search-munition teams, IEEE Intelligent Systems, 24(3), 10-13. Velagapudi, P., Owens, S., Scerri, P., Sycara, K., & Lewis, M. (2009). Environmental factors affecting situation awareness in unmanned aerial vehicles, AIAA Unmanned..Unlimited Conference, April 6-9. Lewis, M. and Wang, J. (2009). Measuring coordination demand in multirobot teams, Proceedings of the 53rd Annual Meeting of the Human Factors and Ergonomics Society, October. Reitter, D., Lebiere, C., Lewis, M., Wang, H., and Ma, Z. (2009). A cognitive model of visual path planning in a multi-robot control system, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, October. Velagapudi, P., Wang, H., Lewis, M., Scerri, P., and Sycara, K. (2009). Scaling Effects for Streaming Video vs. Static Panorama in Multirobot Search, The 2009 IEEE/RSJ International Conference on Intelligent RObots and Systems, October. Wang, H., Lewis, M., Velagapudi, P., Scerri, P., and Sycara, K. (2009). Scaling effects for synchronous vs. asynchronous video in multi-robot search, Proceedings of the 53rd Annual Meeting of the Human Factors and Ergonomics Society, October. Wang, H., Chien, S., Lewis, M., Velagapudi, P., Scerri, P., and Sycara, K. (2009). Human teams for large scale multirobot control, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, October. First Year Review

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