Surface recognition is essential for legged robots because they need to maintain their dynamic balance on a regular or uneven terrain. The accelerometer is a widely-used tool for this purpose, but the quadruped Sony AIBO does not have such a high-end sensor compared to the latest state-of-art developments. Past works focused on attaching replacement sensors to the robot dog as well as collecting many samples for machine learning methods although some studies did not address this issue at all. This paper focuses on improvements with sensor fusion of built-in sensors to recognize wider variety of surfaces and get similar or better accuracy than earlier experiments. The combined features are based on the accelerometer and paw sensors to make the recognition more robust and a naive Bayes classifier achieves 85-91% accuracy for different locomotion speeds. Evaluation suggests that this method can reduce the data collection time for training samples dramatically and it is suitable for practical applications.
It is important to understand how the cultural background, the age and the gender influence the expectations towards social robots. Although past works studied the user adaptation for some months, the users with multiple years of ownership (heavy users) were not subjects of any experiment to compare these criteria over the years. This exploratory research examines the owners of the discontinued Sony AIBO because these robots have not been abandoned by some enthusiastic users and they are still resold on the secondhand market. 78 Sony AIBO owners were recruited on-line and their quantitative data were analyzed by four independent variables (age, gender, culture, length of ownership), user contribution and model preference points of view. The results revealed the motives to own these robots for years and how the heavy users perceived their social robots after a long period in the robot acceptance phase.
Abstract-Although the infrared (IR) range and motor force sensors have been rarely applied to the surface recognition of mobile robots, they are fused in this paper with accelerometer and ground contact force sensors to distinguish six indoor surface types. Their sensor values are affected by the crawling gait period, therefore, certain components of the fast Fourier transform over these data are included in the feature vectors as well as remarkable discriminative power is observed for the same scalar statistics of different sensing modalities. The machine learning aspects are analyzed with random forests (RF) because of their stable performance and some inherent, beneficial properties for the model development process. The robustness is evaluated with unseen data after the model accuracy is estimated with cross-validation (CV), and regardless whether a Sony ERS-7 walks barefoot or wears socks, the forests achieve 94% accuracy. This result outperforms the state of the art techniques for indoor surfaces in the literature and the classification execution is real-time on the robot. The above mentioned model development process with RF is documented to create new models for other robots more quickly and efficiently.
Abstract. Despite the recent technological advances, long-term experiments with robots have challenges to keep the users interested after the initial excitement disappears. This paper explores the user expectations by analyzing the long-term owners of Sony AIBO who have been using these robots for years (heavy users). 78 participants filled an on-line questionnaire and their answers were inspected to discover the key needs of this user group. Quantitative and textual methods confirmed that the most-wanted skills were the interaction with humans and the autonomous operation. The integration with the AI agents and Internet services was important, but the long-term memory and learning capabilities were not that relevant for the participants as expected. The diverse preferences between robot skills led to the creation of a prioritized recommendation list to complement the design guidelines for social robots in the literature.
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