1. John Anderson. Humanoid Multi-Robot Systems. In Prahlad Vadakkepat and Ambarish Goswami, editors, Humanoid Robotics: A Rererence, pages 2473-2481. Springer-Verlag, Heidelberg, 2018. Abstract:

   The ability to function socially, both directly in groups and indirectly through understanding the needs and perspectives of others, is an important part of intelligent behavior. This chapter introduces important elements of multi-agent and multi-robot systems and focuses on the particular issues brought about when humanoid robots are employed. Previous work using humanoid robots - both inside and outside of robotics competitions - is reviewed, and open problems are discussed.

   
   

   @incollection{Anderson18:LocalGroundedCommChapter09,
   author = {John Anderson},
   editor = {Prahlad Vadakkepat and Ambarish Goswami},
   title = {Humanoid Multi-Robot Systems},
   booktitle = {Humanoid Robotics: A Rererence},
   publisher = {Springer-Verlag},
   year = {2018},
   isbn = {978-3-540-89932-7},
   pages = {2473--2481},
   address = {Heidelberg},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/HumanoidMRS.pdf},
   abstract = {The ability to function socially, both directly in groups and indirectly through understanding the needs and perspectives of others, is an important part of intelligent behavior. This chapter introduces important elements of multi-agent and multi-robot systems and focuses on the particular issues brought about when humanoid robots are employed. Previous work using humanoid robots - both inside and outside of robotics competitions - is reviewed, and open problems are discussed.} 
   }
   

1. Amir Hosseinmemar, Jacky Baltes, John Anderson, Meng Cheng Lau, Chi Fung Lun, and Ziang Wang. Closed-Loop Push Recovery for an Inexpensive Humanoid Robot. In Proceedings of the 31st International Conference on Industrial, Engineering, and Other Applications of Intelligent Systems (IEAAIE-18, Montreal, Quebec, pages 233-244, June 2018. Note: Best Paper Award. Abstract:

   Active balancing in autonomous humanoid robots is a challenging task due to the complexity of combining a walking gait with dynamic balancing, vision and high-level behaviors. Humans not only walk successfully over even and uneven terrain, but can recover from the interaction of external forces such as impacts with obstacles and active pushes. While push recovery has been demonstrated successfully in expensive robots, it is more challenging with robots that are inexpensive, with limited power in actuators and less accurate sensing. This work describes a closed-loop control method that uses an accelerometer and gyroscope to allow an inexpensive humanoid robot to actively balance while walking and recover from pushes. An experiment is performed to test three hand-tuned closed-loop control configurations; using only a the gyroscope, only the accelerometer, and a combination of both sensors to recover from pushes. Experimental results show that the combination of gyroscope and accelerometer outperforms the other methods with 100% recovery from a light push and 70% recovery from a strong push.

   
   

   @inproceedings{IEAAIE18:PR,
   author = {Amir Hosseinmemar and Jacky Baltes and John Anderson and Meng Cheng Lau and Chi Fung Lun and Ziang Wang},
   booktitle = {Proceedings of the 31st International Conference on Industrial, Engineering, and Other Applications of Intelligent Systems (IEAAIE-18},
   title = {Closed-Loop Push Recovery for an Inexpensive Humanoid Robot},
   year = {2018},
   address = {Montreal, Quebec},
   month = {June},
   pages = {233--244},
   note = {Best Paper Award},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/RMIEAAIE.pdf},
   abstract = {Active balancing in autonomous humanoid robots is a challenging task due to the complexity of combining a walking gait with dynamic balancing, vision and high-level behaviors. Humans not only walk successfully over even and uneven terrain, but can recover from the interaction of external forces such as impacts with obstacles and active pushes. While push recovery has been demonstrated successfully in expensive robots, it is more challenging with robots that are inexpensive, with limited power in actuators and less accurate sensing. This work describes a closed-loop control method that uses an accelerometer and gyroscope to allow an inexpensive humanoid robot to actively balance while walking and recover from pushes. An experiment is performed to test three hand-tuned closed-loop control configurations; using only a the gyroscope, only the accelerometer, and a combination of both sensors to recover from pushes. Experimental results show that the combination of gyroscope and accelerometer outperforms the other methods with 100% recovery from a light push and 70% recovery from a strong push. } 
   }
   




2. Kyle J. Morris, John Anderson, Meng Cheng Lau, and Jacky Baltes. Interaction and Learning in a Humanoid Robot Magic Performance. In Siddharth Srivastava, editor, Proceedings of the AAAI Spring Symposium on Integrating Representation, Readoning, Learning and Execution for Goal-Directed Autonomy, Stanford, CA, pages 578-581, March 2018. Abstract:

   Magicians have been a source of entertainment for many centuries, with the ability to play on human bias, and perception to create an entertaining experience. There has been rapid growth in robotics throughout industrial applications; where primary challenges in- clude improving human-robot interaction, and robotic perception. Despite preliminary work in expressive AI, which aims to use AI for entertainment; there has not been direct application of fully embodied autonomous agents (vision, speech, learning, planning) to enter- tainment domains. This paper describes preliminary work towards the use of magic tricks as a method for developing fully-embodied autonomous agents. A card trick is developed requiring vision, communica- tion, interaction, and learning capabilities all of which are coordinated using our script representation. Our work is evaluated quantitatively through experimen- tation, and qualitatively through acquiring 2nd place at the 2016 IROS Humanoid Application Challenge. A video of the live performance can be found at https://youtu.be/OMpcmcPWAVM

   
   

   @inproceedings{morrisAAAISS18,
   author = {Kyle J. Morris and John Anderson and Meng Cheng Lau and Jacky Baltes},
   title = {Interaction and Learning in a Humanoid Robot Magic Performance},
   booktitle = {Proceedings of the AAAI Spring Symposium on Integrating Representation, Readoning, Learning and Execution for Goal-Directed Autonomy},
   editors = {Siddharth Srivastava},
   address = {Stanford, CA},
   month = {March},
   pages = {578--581},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/IntLearningMagic18.pdf},
   year = {2018},
   abstract = {Magicians have been a source of entertainment for many centuries, with the ability to play on human bias, and perception to create an entertaining experience. There has been rapid growth in robotics throughout industrial applications; where primary challenges in- clude improving human-robot interaction, and robotic perception. Despite preliminary work in expressive AI, which aims to use AI for entertainment; there has not been direct application of fully embodied autonomous agents (vision, speech, learning, planning) to enter- tainment domains. This paper describes preliminary work towards the use of magic tricks as a method for developing fully-embodied autonomous agents. A card trick is developed requiring vision, communica- tion, interaction, and learning capabilities all of which are coordinated using our script representation. Our work is evaluated quantitatively through experimen- tation, and qualitatively through acquiring 2nd place at the 2016 IROS Humanoid Application Challenge. A video of the live performance can be found at https://youtu.be/OMpcmcPWAVM } 
   }
   




3. Kyle J. Morris, Vladyslav Samonin, John Anderson, Meng Cheng Lau, and Jacky Baltes. Robot Magic: A Robust Interactive Humanoid Entertainment Robot. In Proceedings of the 31st International Conference on Industrial, Engineering, and Other Applications of Intelligent Systems (IEAAIE-18, Montreal, Quebec, pages 245-256, June 2018. Note: Best Paper Award. Abstract:

   In recent years, there have been a number of popular robotics competitions whose intent is to advance the state of research by comparing embodied entries against one another in real time. The IEEE Humanoid application challenge is intended to broaden these by allowing more open-ended entries, with a general theme within which entrants are ective application involving a humanoid robot. This year's theme was Robot Magic, and this paper describes our rst-place winning entry in the 2017 competition, running on a ROBOTIS OP2 humanoid robot. We describe the overall agent design and contributions to perception, learning, control, and representation, which together support a robust live robot magic performance.

   
   

   @inproceedings{IEAAIE18:RM,
   author = {Kyle J. Morris and Vladyslav Samonin and John Anderson and Meng Cheng Lau and Jacky Baltes},
   booktitle = {Proceedings of the 31st International Conference on Industrial, Engineering, and Other Applications of Intelligent Systems (IEAAIE-18},
   title = {Robot Magic: A Robust Interactive Humanoid Entertainment Robot},
   year = {2018},
   address = {Montreal, Quebec},
   month = {June},
   pages = {245--256},
   note = {Best Paper Award},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/RMIEAAIE.pdf},
   abstract = {In recent years, there have been a number of popular robotics competitions whose intent is to advance the state of research by comparing embodied entries against one another in real time. The IEEE Humanoid application challenge is intended to broaden these by allowing more open-ended entries, with a general theme within which entrants are ective application involving a humanoid robot. This year's theme was Robot Magic, and this paper describes our rst-place winning entry in the 2017 competition, running on a ROBOTIS OP2 humanoid robot. We describe the overall agent design and contributions to perception, learning, control, and representation, which together support a robust live robot magic performance.} 
   }
   

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