Analyzing the Impact of Different Feature Queries in Active Learning for Social Robots

González-Pacheco, Víctor; Malfáz, María; Castro‐González, Álvaro; Castillo, José Carlos; Alonso, Francisco; Salichs, Miguel Á.

doi:10.1007/s12369-017-0449-0

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AL techniques have been used to economically learn robot policies [21,22,55,62] and task representations [25,34,41,53], to guide efficient information gathering [17], and to learn reward functions by querying the user with different type of queries [9,10,24,59]. Another line of research has instead concentrated on the interactive nature of AL, with work investigating the design of active robot learners [18,19], the ability of users to answer the robot's questions [32,57] and the development of AL strategies that take into account the user in their query selection [11,38,54]. New types of queries, different from asking for a positive or negative label, have been adopted or developed through this line of research, including demonstration queries [16,19], feature queries [10,16,19], and comparison queries [12,59].…”

Section: Related Workmentioning

confidence: 99%

Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization

Racca

Kyrki

Çakmak

2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

Enabling diverse users to program robots for different applications is critical for robots to be widely adopted. Most of the new collaborative robot manipulators come with intuitive programming interfaces that allow novice users to compose robot programs and tune their parameters. However, parameters like motion speeds or exerted forces cannot be easily demonstrated and often require manual tuning, resulting in a tedious trial-and-error process. To address this problem, we formulate tuning of one-dimensional parameters as an Active Learning problem where the learner iteratively refines its estimate of the feasible range of parameter values, by selecting informative queries. By executing the parametrized actions, the learner gathers the user's feedback, in the form of directional answers ("higher, " "lower, " or "fine"), and integrates it in the estimate. We propose an Active Learning approach based on Expected Divergence Maximization for this setting and compare it against two baselines with synthetic data. We further compare the approaches on a real-robot dataset obtained from programs written with a simple Domain-Specific Language for a robot arm and manually tuned by expert users (N=8) to perform four manipulation tasks. We evaluate the effectiveness and usability of our interactive tuning approach against manual tuning with a user study where novice users (N=8) tuned parameters of a human-robot hand-over program. CCS CONCEPTS• Computing methodologies → Active learning settings; • Human-centered computing → User centered design; • Computer systems organization → External interfaces for robotics.

show abstract

Section: Related Workmentioning

confidence: 99%

Interactive Tuning of Robot Program Parameters via Expected Divergence Maximization

Racca

Kyrki

Çakmak

2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

show abstract

“…Another line of research looks instead at the nature of the interaction with AL robots. Aspects like the nature of robot's queries [10] and of the query selection mechanism [12], [24], and their impact on the quality of user's answers [25], [26] have been studied. Also the balance of control between learner and teacher during the interaction [9], the effect of queries' timing [27] and the transparency of the learning process [28] have been investigated.…”

Section: Related Workmentioning

confidence: 99%