| 1. |
- Björnfot, Patrik, et al.
(författare)
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Probing the design space of a telepresence robot gesture arm with low fidelity prototypes
- 2017
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Ingår i: HRI '17. - New York, NY, USA : ACM Digital Library. - 9781450343367 ; , s. 352-360
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Bokkapitel (refereegranskat)abstract
- The general problem addressed in this paper is supporting a more efficient communication between remote users, who control telepresence robots, and people in the local setting. The design of most telepresence robots does not allow them to perform gestures. Given the key role of pointing in human communication, exploring design solutions for providing telepresence robots with deictic gesturing capabilities is, arguably, a timely research issue for Human-Robot Interaction. To address this issue, we conducted an empirical study, in which a set of low fidelity prototypes, illustrating various designs of a robot's gesture arm, were assessed by the participants (N=18). The study employed a mixed-method approach, a combination of a controlled experiment, elicitation study, and design provocation. The evidence collected in the study reveals participants' assessment of the designs, used in the study, and provides insights into parti.cipants' attitudes and expectations regarding gestural communication with telepresence robots in general.
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| 2. |
- Kragic, Danica, 1971-
(författare)
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Acting, Interacting, Collaborative Robots
- 2017
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Ingår i: PROCEEDINGS OF THE 2017 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'17). - New York, NY, USA : IEEE. - 9781450343367 ; , s. 293-293
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Konferensbidrag (refereegranskat)abstract
- The current trend in computer vision is development of data-driven approaches where the use of large amounts of data tries to compensate for the complexity of the world captured by cameras. Are these approaches also viable solutions in robotics? Apart from 'seeing', a robot is capable of acting, thus purposively change what and how it sees the world around it. There is a need for an interplay between processes such as attention, segmentation, object detection, recognition and categorization in order to interact with the environment. In addition, the parameterization of these is inevitably guided by the task or the goal a robot is supposed to achieve. In this talk, I will present the current state of the art in the area of robot vision and discuss open problems in the area. I will also show how visual input can be integrated with proprioception, tactile and force-torque feedback in order to plan, guide and assess robot's action and interaction with the environment. Interaction between two agents builds on the ability to engage in mutual prediction and signaling. Thus, human-robot interaction requires a system that can interpret and make use of human signaling strategies in a social context. Our work in this area focuses on developing a framework for human motion prediction in the context of joint action in HRI. We base this framework on the idea that social interaction is highly influences by sensorimotor contingencies (SMCs). Instead of constructing explicit cognitive models, we rely on the interaction between actions the perceptual change that they induce in both the human and the robot. This approach allows us to employ a single model for motion prediction and goal inference and to seamlessly integrate the human actions into the environment and task context. We employ a deep generative model that makes inferences over future human motion trajectories given the intention of the human and the history as well as the task setting of the interaction. With help predictions drawn from the model, we can determine the most likely future motion trajectory and make inferences over intentions and objects of interest.
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| 3. |
- Skantze, Gabriel
(författare)
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Predicting and Regulating Participation Equality in Human-robot Conversations : Effects of Age and Gender
- 2017
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Ingår i: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. - New York, NY, USA : IEEE Computer Society. - 9781450343367 ; , s. 196-204
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Konferensbidrag (refereegranskat)abstract
- In this paper, we investigate participation equality, in terms of speaking time, between users in multi-party human-robot conversations. We analyse a dataset where pairs of users (540 in total) interact with a conversational robot exhibited at a technical museum. The data encompass a wide range of different users in terms of age (adults/children) and gender (male/female), in different combinations. Overall, the analysis indicates that demographically heterogeneous pairs are more imbalanced, especially pairs of adults and children, where children are less prone to self-select in the turn-taking. The analysis also indicates that it is possible for the robot to reduce the imbalance by addressing the least dominant user and asking directed questions. However, for children to respond, it is important to seek mutual gaze and switch addressee often. Finally, we show that it is possible to predict the imbalance at an early stage in the interaction - in order to increase the participation equality as early as possible - and that knowledge about the users' age and gender helps in this prediction.
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| 4. |
- Stenmark, Maj, et al.
(författare)
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Simplified Programming of Re-usable Skills on a Safe Industrial Robot -- Prototype and Evaluation
- 2017
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Ingår i: Proceedings of the IEEE/ACM Conference on Human-Robot Interaction (HRI). - New York, NY, USA : ACM. - 9781450343367 ; , s. 463-472
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Konferensbidrag (refereegranskat)abstract
- This paper presents a study on iconic programming support for mainly position-based lead-through programming of an ABB YuMi collaborative robot. A prototype tool supporting a hybrid programming and execution mode was developed and evaluated with 21 non-expert users with varying programming and robotics experience. We also present a comparison of the programming times for an expert robot programmer using traditional tools versus the new tool. The expert programmed the same tasks in 1/5 of the time compared to traditional tools and the non-experts were able to program and debug a LEGO building task using the robot within 30 minutes.
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