Analyzing Trigger-Action Programming for Personalization of Robot Behaviour in IoT Environments

Manca, Marco; Paternò, Fabio; Santoro, Carmen

doi:10.1007/978-3-030-24781-2_7

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Our work shares the goal of EUP for robots, which is to enable people with little or no programming background to be able to program robots with as little effort as possible. Work in this area has produced tools that allow novice users to combine robot primitives or actions to create complex programs, with input modalities ranging from visual programming [8,31,35,36,45,46,64,65], kinesthetic teaching [1,2,60,66], and natural language commands [13,26,33]. The robot actions available to compose programs are often parametrized, with number and complexity of the parameters depending on the level of abstraction adopted in the action design.…”

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

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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.

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