Human-in-the-loop Pose Estimation via Shared Autonomy
| Autor: | Zhefan Ye, Jean Y. Song, Odest Chadwicke Jenkins, Walter S. Lasecki, Jorge Vilchis, Zhiqiang Sui, Stephen Hart |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Process (engineering) business.industry Monte Carlo localization Pointing device 02 engineering and technology Object (computer science) 01 natural sciences Automation 010104 statistics & probability 020901 industrial engineering & automation Human–computer interaction Robot Human-in-the-loop 0101 mathematics business Pose |
| Zdroj: | IUI |
| DOI: | 10.1145/3397481.3450654 |
| Popis: | Reliable, efficient shared autonomy requires balancing human operation and robot automation on complex tasks, such as dexterous manipulation. Adding to the difficulty of shared autonomy is a robot’s limited ability to perceive the 6 degree-of-freedom pose of objects, which is essential to perform manipulations those objects afforded. Inspired by Monte Carlo Localization, we propose a generative human-in-the-loop approach to estimating object pose. We characterize the performance of our mixed-initiative 3D registration approach using 2D pointing devices via a user study. Seeking an analog for Fitts’s Law for 3D registration, we introduce a new evaluation framework that takes the entire registration process into account instead of only the outcome. When combined with estimates of registration confidence, we posit that mixed-initiative registration will reduce the human workload while maintaining or even improving final pose estimation accuracy. |
| Databáze: | OpenAIRE |
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