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.

In this paper, we report on the findings of a human-robot interaction study that aims at developing a communication language for transferring grasping skills from a nontechnical user to a robot. Participants with different backgrounds and education levels were asked to command a five-degree-of-freedom human-scale robot arm to grasp five small everyday objects. They were allowed to use either commands from an existing command set or develop their own equivalent natural language instructions. The study revealed several important findings. First, individual participants were more inclined to use simple, familiar commands than more powerful ones. In most cases, once a set of instructions was found to accomplish the grasping task, few participants deviated from that set. In addition, we also found that the participant's background does appear to play a role during the interaction process. Overall, participants with less technical backgrounds require more time and more commands on average to complete a grasping task as compared to participants with more technical backgrounds.

In this paper, we investigate the impact of the user's background on their ability to communicate grasping commands to a robot. We conducted a study where a group of 15 non-technical users use natural language to instruct a robotic arm to grasp five small everyday objects. We found that users with less technical backgrounds choose simple more predictable commands over complex unpredictable movements. These users also required more time and commands to complete a grasping task compared to users with more technical backgrounds. Other results however suggest that the user's background is not the most critical factor. Individual preferences and learning approaches also appear to play a role in command choices.

In this paper, a pilot study is conducted to explore developing a human-robot interaction language that specifically targets robotic grasping. The short term goal is to help nontechnical users command and control a simple robotic arm to grasp small objects. The long term objective is to use this language to enable skill transfer of grasping skills between nontechnical users and personal service robots. The study included a small group of participants with various technical backgrounds. They were asked to use a primitive set of commands to instruct a CRS robotic arm to grasp five small objects which are typically difficult to grasp. The findings of this pilot study are presented along with further insight gathered from participant feedback.