Jijo-2: An Office Robot that Communicates and Learns
IEEE Intelligent Systems
Predicting UNIX Command Lines: Adjusting to User Patterns
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Interactive humanoid robots for a science museum
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
On the effect of the user's background on communicating grasping commands
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Human to robot demonstrations of routine home tasks: exploring the role of the robot's feedback
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
A point-and-click interface for the real world: laser designation of objects for mobile manipulation
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Superpositioning of behaviors learned through teleoperation
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Toward a Natural Language Interface for Transferring Grasping Skills to Robots
IEEE Transactions on Robotics
The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications
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This paper presents an integrated system that combines learning, a natural-language interface, and robotic grasping to enable the transfer of grasping skills from nontechnical users to robots. The system consists of two parts: a natural-language interface for grasping commands and a learning system. This paper focuses on the learning system and testing of the entire system in a small usability study. The learning system presented consists of two phases. In the first phase, the system learns to predict the next command, which the user is planning to issue based on command sequences recorded during previous grasping sessions. In the second phase, the system predicts the user's current state and moves the robot's gripper to the intended target endpoint to attempt to grasp the object. Using eight nontechnical users and a 5-degree-of-freedom (DOF) robot arm, a usability study was conducted to observe the impact of the learning system on user performance and satisfaction during a grasping operation. Experimental results show that the system was effective in learning users' grasping intentions, which allowed it to reduce the average time to grasp an object. In addition, participants' feedback from the usability study was generally positive toward having an adaptive robotics system that learns from their commands.