The Benefits of Immersive Demonstrations for Teaching Robots

被引:0
作者
Jackson, Astrid [1 ,2 ]
Northcutt, Brandon D. [1 ]
Sukthankar, Gita [2 ]
机构
[1] Toyota Res Inst, Los Altos, CA 94022 USA
[2] Univ Cent Florida, Dept Comp Sci, Orlando, FL 32816 USA
来源
HRI '19: 2019 14TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION | 2019年
关键词
Learning from Demonstration; Imitation Learning; Robot Manipulation; Virtual Reality; User Study;
D O I
10.1109/hri.2019.8673270
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
One of the advantages of teaching robots by demonstration is that it can be more intuitive for users to demonstrate rather than describe the desired robot behavior. However, when the human demonstrates the task through an interface, the training data may inadvertently acquire artifacts unique to the interface, not the desired execution of the task. Being able to use one's own body usually leads to more natural demonstrations, but those examples can be more difficult to translate to robot control policies. This paper quantifies the benefits of using a virtual reality system that allows human demonstrators to use their own body to perform complex manipulation tasks. We show that our system generates superior demonstrations for a deep neural network without introducing a correspondence problem. The effectiveness of this approach is validated by comparing the learned policy to that of a policy learned from data collected via a conventional gaming system, where the user views the environment on a monitor screen, using a Sony Play Station 3 (PS3) DualShock 3 wireless controller as input.
引用
收藏
页码:326 / 334
页数:9
相关论文
共 30 条
  • [1] Abbeel P., 2007, Advances in neural information processing systems, V19, P1
  • [2] Keyframe-based Learning from Demonstration Method and Evaluation
    Akgun, Baris
    Cakmak, Maya
    Jiang, Karl
    Thomaz, Andrea L.
    [J]. INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS, 2012, 4 (04) : 343 - 355
  • [3] Leveraging on a virtual environment for robot programming by demonstration
    Aleotti, J
    Caselli, S
    Reggiani, M
    [J]. ROBOTICS AND AUTONOMOUS SYSTEMS, 2004, 47 (2-3) : 153 - 161
  • [4] Grasp recognition in virtual reality for robot pregrasp planning by demonstration
    Aleotti, Jacopo
    Caselli, Stefano
    [J]. 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, 2006, : 2801 - +
  • [5] Argall Brenna D., 2010, Foundations and Trends in Robotics, V1, P79, DOI 10.1561/2300000012
  • [6] A survey of robot learning from demonstration
    Argall, Brenna D.
    Chernova, Sonia
    Veloso, Manuela
    Browning, Brett
    [J]. ROBOTICS AND AUTONOMOUS SYSTEMS, 2009, 57 (05) : 469 - 483
  • [7] Billard A, 2003, IROS 2003: PROCEEDINGS OF THE 2003 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, P2398
  • [8] Bishop C., 1994, MIXTURE DENSITY NETW
  • [9] Calinon S, 2007, INTERACT STUD, V8, P441
  • [10] Chernova Sonia, 2014, SYNTHESIS LECT ARTIF, V8, P1, DOI [DOI 10.2200/S00568ED1V01Y201402AIM028, 10.2200/S00568ED1V01Y201402AIM028]