Physical interaction between humans and robots can help robots learn to perform complex tasks. The robot arm gains information by observing how the human kinesthetically guides it throughout the task. While prior works focus on how the robot learns, it is equally important that this learning is transparent to the human teacher. Visual displays that show the robot’s uncertainty can potentially communicate this information; however, we hypothesize that visual feedback mechanisms miss out on the physical connection between the human and robot. In this work we present a soft haptic display that wraps around and conforms to the surface of a robot arm, adding a haptic signal at an existing point of contact without significantly affecting the interaction. We demonstrate how soft actuation creates a salient haptic signal while still allowing flexibility in device mounting. Using a psychophysics experiment, we show that users can accurately distinguish inflation levels of the wrapped display with an average Weber fraction of 11.4%. When we place the wrapped display around the arm of a robotic manipulator, users are able to interpret and leverage the haptic
signal in sample robot learning tasks, improving identification of areas where the robot needs more training and enabling the user to provide better demonstrations. See videos of our device and user studies here: https://youtu.be/tX-2Tqeb9Nw
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This content will become publicly available on June 1, 2024
Kinesthetic Feedback for Understanding Program Execution
To better prepare future generations, knowledge about computers and programming are one of the many skills that are part of almost all Science, Technology, Engineering, and Mathematic programs; however, teaching and learning programming is a complex task that is generally considered difficult by students and teachers alike. One approach to engage and inspire students from a variety of backgrounds is the use of educational robots. Unfortunately, previous research presents mixed results on the effectiveness of educational robots on student learning. One possibility for this lack of clarity may be because students have a wide variety of styles of learning. It is possible that the use of kinesthetic feedback, in addition to the normally used visual feedback, may improve learning with educational robots by providing a richer, multi-modal experience that may appeal to a larger number of students with different learning styles. It is also possible, however, that the addition of kinesthetic feedback, and how it may interfere with the visual feedback, may decrease a student’s ability to interpret the program commands being executed by a robot, which is critical for program debugging. In this work, we investigated whether human participants were able to accurately determine a sequence of program commands performed by a robot when both kinesthetic and visual feedback were being used together. Command recall and end point location determination were compared to the typically used visual-only method, as well as a narrative description. Results from 10 sighted participants indicated that individuals were able to accurately determine a sequence of movement commands and their magnitude when using combined kinesthetic + visual feedback. Participants’ recall accuracy of program commands was actually better with kinesthetic + visual feedback than just visual feedback. Although the recall accuracy was even better with the narrative description, this was primarily due to participants confusing an absolute rotation command with a relative rotation command with the kinesthetic + visual feedback. Participants’ zone location accuracy of the end point after a command was executed was significantly better for both the kinesthetic + visual feedback and narrative methods compared to the visual-only method. Together, these results suggest that the use of both kinesthetic + visual feedback improves an individual’s ability to interpret program commands, rather than decreases it.
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- Award ID(s):
- 1742242
- NSF-PAR ID:
- 10461741
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 23
- Issue:
- 11
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 5159
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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