Training Human Teacher to Improve Robot Learning from Demonstration: A Pilot Study on Kinesthetic Teaching

Sakr, Maram; Freeman, Martin; Croft, Elizabeth A.

doi:10.1109/ro-man47096.2020.9223430

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…In addition, practice makes a significant difference in slow-adapters' performance, approaching fastadapters' performance. This finding agrees with our previous work demonstrating the need for training for slow-adapters to provide high-quality demonstrations for robots [25].…”

Section: Discussionsupporting

confidence: 93%

Quantifying Demonstration Quality for Robot Learning and Generalization

Sakr¹,

Li²,

Kulić³

et al. 2022

Preprint

Self Cite

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Learning from Demonstration (LfD) seeks to democratize robotics by enabling diverse end-users to teach robots to perform a task by providing demonstrations. However, most LfD techniques assume users provide optimal demonstrations. This is not always the case in real applications where users are likely to provide demonstrations of varying quality, that may change with expertise and other factors. Demonstration quality plays a crucial role in robot learning and generalization. Hence, it is important to quantify the quality of the provided demonstrations before using them for robot learning. In this paper, we propose quantifying the quality of the demonstrations based on how well they perform in the learned task. We hypothesize that task performance can give an indication of the generalization performance on similar tasks. The proposed approach is validated in a user study (N = 27). Users with different robotics expertise levels were recruited to teach a PR2 robot a generic task (pressing a button) under different task constraints. They taught the robot in two sessions on two different days to capture their teaching behaviour across sessions. The task performance was utilized to classify the provided demonstrations into high-quality and lowquality sets. The results show a significant Pearson correlation coefficient (ρ = 0.85, p < 0.0001) between the task performance and generalization performance across all participants. We also found that users clustered into two groups: Users who provided high-quality demonstrations from the first session, assigned to the fast-adapters group, and users who provided low-quality demonstrations in the first session and then improved with practice, assigned to the slow-adapters group. These results highlight the importance of quantifying demonstration quality, which can be indicative of the adaptation level of the user to the task.

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Section: Discussionsupporting

confidence: 93%

Quantifying Demonstration Quality for Robot Learning and Generalization

Sakr¹,

Li²,

Kulić³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We believe that the results of this paper open up several directions for future research. The significant improvement in the efficiency by only guiding users to well distribute the demonstrations suggests that guiding users to provide high-quality demonstrations as in [35] along with their good distribution could further boost learning efficiency. It will also be interesting to test the proposed approach in facilities with real users without controlling the conditions.…”

Section: Discussionmentioning

confidence: 99%

What Makes a Good Demonstration for Robot Learning Generalization?

Sakr

Kulić

Croft

2021

Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

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Learning from Demonstration (LfD) is a framework that allows lay users to easily program robots. However, the efficiency of robot learning and the robot's ability to generalize to task variations hinges upon the quality and quantity of the provided demonstrations. Our objective is to guide human teachers to furnish more effective demonstrations, thus facilitating efficient robot learning. To achieve this, we propose to use a measure of uncertainty, namely task-related information entropy, as a criterion for suggesting informative demonstration examples to human teachers to improve their teaching skills. In a conducted experiment (N=24), an augmented reality (AR)based guidance system was employed to train novice users to produce additional demonstrations from areas with the highest entropy within the workspace. These novice users were trained for a few trials to teach the robot a generalizable task using a limited number of demonstrations. Subsequently, the users' performance after training was assessed first on the same task (retention) and then on a novel task (transfer) without guidance. The results indicated a substantial improvement in robot learning efficiency from the teacher's demonstrations, with an improvement of up to 198% observed on the novel task. Furthermore, the proposed approach was compared to a state-of-the-art heuristic rule and found to improve robot learning efficiency by 210% compared to the heuristic rule.

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“…Prior work has suggested that training interventions may accelerate the development of skills necessary for kinesthetic teaching compared to open-ended practice (e.g., [23,24]). Existing work on education for kinesthetic teaching has focused on understanding the effectiveness of different modalities (i.e., visual or haptic) for training or understanding how to prime endusers to develop kinesthetic demonstrations that are effective for subsequent task-level robot learning (e.g., [15,16]).…”

Section: Educational Tools For End-user Robot Programmingmentioning

confidence: 99%

Curricula for teaching end-users to kinesthetically program collaborative robots

Ajaykumar,

Hager,

Huang

2023

PLoS ONE

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Non-expert users can now program robots using various end-user robot programming methods, which have widened the use of robots and lowered barriers preventing robot use by laypeople. Kinesthetic teaching is a common form of end-user robot programming, allowing users to forgo writing code by physically guiding the robot to demonstrate behaviors. Although it can be more accessible than writing code, kinesthetic teaching is difficult in practice because of users’ unfamiliarity with kinematics or limitations of robots and programming interfaces. Developing good kinesthetic demonstrations requires physical and cognitive skills, such as the ability to plan effective grasps for different task objects and constraints, to overcome programming difficulties. How to help users learn these skills remains a largely unexplored question, with users conventionally learning through self-guided practice. Our study compares how self-guided practice compares with curriculum-based training in building users’ programming proficiency. While we found no significant differences between study participants who learned through practice compared to participants who learned through our curriculum, our study reveals insights into factors contributing to end-user robot programmers’ confidence and success during programming and how learning interventions may contribute to such factors. Our work paves the way for further research on how to best structure training interventions for end-user robot programmers.

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Training Human Teacher to Improve Robot Learning from Demonstration: A Pilot Study on Kinesthetic Teaching

Cited by 12 publications

References 27 publications

Quantifying Demonstration Quality for Robot Learning and Generalization

Quantifying Demonstration Quality for Robot Learning and Generalization

What Makes a Good Demonstration for Robot Learning Generalization?

Curricula for teaching end-users to kinesthetically program collaborative robots

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