Ranger, An Example of Integration of Robotics into the Home Ecosystem

Mondada, Francesco; Fink, Julia; Lemaignan, Séverin; Mansolino, David; Wille, Florian; Franinovic, Karmen

doi:10.1007/978-3-319-23832-6_15

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…Based on this previous research, we explore in this study how to sustain children's engagement with the Ranger robot [9] by manipulating the behavior of the robot so that it appears unexpected to the children. The main outcomes of this research are 1) a new experimental task that suggests and contrasts three types of mis-behaviors, with different cognitive correlates, 2) a mixed technique, blending behavioral cues and reported phenomenological perceptions, to assess the robot perception in terms of both engagement and human-likeliness, 3) an actionable approach based on the introduction of mis-behaviors to support child-robot engagement, 4) and a first experimental cue that anthropomorphic perceptions do not necessarily correlate with actual engagement.…”

Section: Towards Sustained Engagementmentioning

confidence: 99%

“…The challenge for the children consists in collecting domino tiles spread over the room, hidden behind beanbags (task of the searcher child), getting the robot to carry to the second child, and finally assemble the tiles and decide for the next tile to fetch (tasks of the receiver child). The Ranger [9] is a wheeled box (27 x 37 x 37 cm) with partial wooden surface. It can drive on a flat surface, move its eyes and eyebrows, display colours (LED arrays) and light patterns, and play sounds through Bluetooth speakers.…”

Section: Experimental Settingmentioning

confidence: 99%

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You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

et al. 2015

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Abstract. We present a study on the impact of unexpected robot behaviors on the perception of a robot by children and their subsequent engagement in a playful interaction based on a novel "domino" task. We propose an original analysis methodology which blends behavioral cues and reported phenomenological perceptions into a compound index. While we found only a limited recognition of the different misbehaviors of the robot that we attribute to the age of the child participants (4-5 years old), interesting findings include a sustained engagement level, an unexpectedly low level of attribution of higher cognitive abilities and a negative correlation between anthropomorphic projections and actual behavioral engagement.

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Section: Towards Sustained Engagementmentioning

confidence: 99%

Section: Experimental Settingmentioning

confidence: 99%

You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

et al. 2015

Self Cite

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“…Each factor is calculated at each time step and the resulting series is averaged over the total number of time steps in the evaluation period. We conducted our experiments using the Ranger robot [12] (see Fig. 1), a wooden robotic box equipped with wheels and sensors aimed to interact with children and motivate them to tidy up their room [20].…”

Section: Benchmark Taskmentioning

confidence: 99%

“…In this article, we use the Ranger robot [12], a small limited robotic platform designed to interact with children, since it has various characteristics which make it interesting for this research. First, it is equipped with a depth-camera used as unique input for our controller.…”

Section: Introductionmentioning

confidence: 99%

Noise-resistant particle swarm optimization for the learning of robust obstacle avoidance controllers using a depth camera

Navarro

Mario

Martinoli

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization problem. Their aim is to increase the robustness of the generated robotic controllers, as compared to previous PSO algorithms. We show that we can successfully apply this set of PSO algorithms to learn 166 parameters of a robotic controller for the obstacle avoidance task. We also study the impact that an increased evaluation budget has on the robustness and average performance of the optimized controllers. Finally, we validate the control solutions learned in simulation by testing the most robust controller in three different real arenas.

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“…In this article, we focus on the combination of both aspects, implementing a reciprocal human-robot aware navigation on the ranger robot [3], a small limited robotic platform designed to interact with children. To our knowledge, this is the first work in which both human-robot and robothuman awareness are implemented on-board on a resourceconstrained robot without the need of external devices such as external cameras, special trackers, etc.…”

Section: Introductionmentioning

confidence: 99%

On-board human-aware navigation for indoor resource-constrained robots: A case-study with the ranger

Talebpour¹,

Navarro²,

Martinoli³

2015

2015 IEEE/SICE International Symposium on System Integration (SII)

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Abstract-Introducing simple robotic platforms into domestic environments is faced with the challenge of social acceptability. Therefore human-aware navigation is a must for robots operating in environments shared with human users. In this work, we focus on the human-aware navigation problem in a structured environment for a robot with limited sensing and constrained maneuvering called Ranger. The Ranger is a simple domestic robotic platform designed for interacting with children. The system combines person detection and tracking -which is the result of fusing laser-scan and depth-image based detectors provided by an RGB-D camera-, basic autonomous navigation and the concept of personal space. We rely only on the on-board sensors for mapping, localization, human tracking, and navigation. Systematic experiments are carried out with a real robot in the presence of a human in order to compare our human-aware navigation with a non human-aware simple approach. The results show that human-aware navigation is able to achieve trajectories which are respecting the personal spaces of the human and are thus more acceptable for the users.

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Ranger, An Example of Integration of Robotics into the Home Ecosystem

Cited by 13 publications

References 15 publications

You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

Noise-resistant particle swarm optimization for the learning of robust obstacle avoidance controllers using a depth camera

On-board human-aware navigation for indoor resource-constrained robots: A case-study with the ranger

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