We introduce a novel humanoid robotic platform designed to jointly address three central goals of humanoid robotics: 1) study the role of morphology in biped locomotion; 2) study full-body compliant physical human-robot interaction; 3) be robust while easy and fast to duplicate to facilitate experimentation. The taken approach relies on functional modeling of certain aspects of human morphology, optimizing materials and geometry, as well as on the use of 3D printing techniques. In this article, we focus on the presentation of the design of specific morphological parts related to biped locomotion: the hip, the thigh, the limb mesh and the knee. We present initial experiments showing properties of the robot when walking with the physical guidance of a human.

This paper studies the impact of interfaces, allowing nonexpert users to efficiently and intuitively teach a robot to recognize new visual objects. We present challenges that need to be addressed for real-world deployment of robots capable of learning new visual objects in interaction with everyday users. We argue that in addition to robust machine learning and computer vision methods, well-designed interfaces are crucial for learning efficiency. In particular, we argue that interfaces can be key in helping nonexpert users to collect good learning examples and, thus, improve the performance of the overall learning system. Then, we present four alternative human-robot interfaces: Three are based on the use of a mediating artifact (smartphone, wiimote, wiimote and laser), and one is based on natural human gestures (with a Wizard-of-Oz recognition system). These interfaces mainly vary in the kind of feedback provided to the user, allowing him to understand more or less easily what the robot is perceiving and, thus, guide his way of providing training examples differently. We then evaluate the impact of these interfaces, in terms of learning efficiency, usability, and user's experience, through a real world and large-scale user study. In this experiment, we asked participants to teach a robot 12 different new visual objects in the context of a robotic game. This game happens in a home-like environment and was designed to motivate and engage users in an interaction where using the system was meaningful. We then discuss results that show significant differences among interfaces. In particular, we show that interfaces such as the smartphone interface allows nonexpert users to intuitively provide much better training examples to the robot, which is almost as good as expert users who are trained for this task and are aware of the different visual perception and machine learning issues. We also show that artifact-mediated teaching is significantly more efficient for robot learning, and equally good in terms of usability and user's experience, than teaching thanks to a gesture-based human-like interaction.

In this paper, we present a real world user study of 4 interfaces designed to teach new visual objects to a social robot. This study was designed as a robotic game in order to maintain the user's motivation during the whole experiment. Among the 4 interfaces 3 were based on mediator objects such as an iPhone, a Wiimote and a laser pointer. They also provided the users with different kind of feedback of what the robot is perceiving. The fourth interface was a gesture based interface with a Wizard-of-Oz recognition system added to compare our mediator interfaces with a more natural interaction. Here, we specially studied the impact the interfaces have on the quality of the learning examples and the usability. We showed that providing non-expert users with a feedback of what the robot is perceiving is needed if one is interested in robust interaction. In particular, the iPhone interface allowed non-expert users to provide better learning examples due to its whole visual feedback. Furthermore, we also studied the user's gaming experience and found that in spite of its lower usability, the gestures interface was stated as entertaining as the other interfaces and increases the user's feeling of cooperating with the robot. Thus, we argue that this kind of interface could be well-suited for robotic game.

We developed three interfaces to allow non-expert users to teach name for new visual objects and compare them through user's studies in term of learning efficiency.

In this paper, we present three human-robot interfaces using a handheld device as a mediator object between a human and a robot, allowing the human to intuitively drive the robot and show it objects. One of the interface is based on a virtual keyboard interface on the iPhone, another is a gesture based interface on the iPhone too and the last one is a Wiimote based interface. They were designed to be easy to use, especially by non-expert domestic users, and to span different metaphors of interaction. In order to compare them and study the impact of the metaphor on the interaction, we designed a user-study based on two obstacle courses. Each of the 25 participants performed two courses with two different interfaces (total of 100 trials). Although the three interfaces were rather equally efficient and all considered as satisfying by the participants, the iPhone gesture interface was largely preferred while the Wiimote was poorly chosen due to the impact of the chosen metaphor.