Cooperative Human and Machine Perception in Teleoperated Assembly
ISER '00 Experimental Robotics VII
Recognition of Operator Motions for Real-Time Assistance Using Virtual Fixtures
HAPTICS '03 Proceedings of the 11th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS'03)
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Human-Machine Collaborative Systems for Microsurgical Applications
International Journal of Robotics Research
ICML '06 Proceedings of the 23rd international conference on Machine learning
Intentional motion on-line learning and prediction
Machine Vision and Applications
Position and force augmentation in a telepresence system and their effects on perceived realism
WHC '09 Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
Trajectory prediction: learning to map situations to robot trajectories
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Robot trajectory optimization using approximate inference
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A model of the common-sense theory of intention and personal causation
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Planning-based prediction for pedestrians
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Virtual fixtures: Perceptual tools for telerobotic manipulation
VRAIS '93 Proceedings of the 1993 IEEE Virtual Reality Annual International Symposium
Strategies for human-in-the-loop robotic grasping
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
In shared control teleoperation, the robot assists the user in accomplishing the desired task, making teleoperation easier and more seamless. Rather than simply executing the user's input, which is hindered by the inadequacies of the interface, the robot attempts to predict the user's intent, and assists in accomplishing it. In this work, we are interested in the scientific underpinnings of assistance: we propose an intuitive formalism that captures assistance as policy blending, illustrate how some of the existing techniques for shared control instantiate it, and provide a principled analysis of its main components: prediction of user intent and its arbitration with the user input. We define the prediction problem, with foundations in inverse reinforcement learning, discuss simplifying assumptions that make it tractable, and test these on data from users teleoperating a robotic manipulator. We define the arbitration problem from a control-theoretic perspective, and turn our attention to what users consider good arbitration. We conduct a user study that analyzes the effect of different factors on the performance of assistance, indicating that arbitration should be contextual: it should depend on the robot's confidence in itself and in the user, and even the particulars of the user. Based on the study, we discuss challenges and opportunities that a robot sharing the control with the user might face: adaptation to the context and the user, legibility of behavior, and the closed loop between prediction and user behavior.