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Development of Bio-inspired Underwater Robot with Adaptive Morphology Capable of Multiple Swimming Modes

  • Authors: Paschal, Thibaut André Pierre; Shintake, Jun; Mintchev, Stefano; Floreano, Dario

Bio-inspired underwater robots have several benefits compared to traditional underwater vehicles such as agility, efficiency, and an environmentally friendly body. However, the bio-inspired underwater robots developed so far have a single swimming mode, which may limit their capability to perform different tasks. This paper presents a re-configurable bio-inspired underwater robot that changes morphology to enable multiple swimming modes: octopus-mode and fish-mode. The robot is 60 cm long and 50 cm wide, weighing 2.1 kg, and consists of a re-configurable body and 8 compliant arms that are actuated independently by waterproof servomotors. In the robot, the octopus-mode is expected to perform unique tasks such as object manipulation and ground locomotion as demonstrated in literature, while the fish-mode is promising to swim faster and efficiently to travel long distance. With this platform, we investigate effectiveness of adaptive morphology in bio-inspired underwater robots. For this purpose, we evaluate the robot in terms of the cost of transport and the swimming efficiency of both the morphologies. The fish-mode exhibited a lower cost of transport of 2.2 and higher efficiency of 1.2 % compared to the octopus-mode, illustrating the effect of the multiple swimming modes by adaptive morphology.

Posted on: October 9, 2017

Soft Pneumatic Gelatin Actuator for Edible Robotics

  • Authors: Shintake, Jun; Sonar, Harshal Arun; Piskarev, Egor; Paik, Jamie; Floreano, Dario

We present a fully edible pneumatic actuator based on gelatin-glycerol material. The actuator is monolithic, fabricated via a molding process, and measures 90 mm in length, 20 mm in width, and 17 mm in thickness. Thanks to the material mechanical characteristics similar to those of silicone elastomers, the actuator exhibits a bending angle of 170.3 degrees and a blocked force of 0.34 N at the applied pressure of 25 kPa. These values are comparable to elastomer based pneumatic actuators. As a validation example, two actuators are integrated to form a gripper capable of handling various objects, highlighting the high performance and applicability of the edible actuator. These edible actuators, combined with other recent edible materials and electronics, could lay the foundation for a new type of edible robots.

Posted on: October 3, 2017

Keep on Moving! Exploring Anthropomorphic Effects of Motion during Idle Moments

  • Authors: Asselborn, Thibault Lucien Christian; Johal, Wafa; Dillenbourg, Pierre

In this paper, we explored the effect of a robot’s subconscious gestures made during moments when idle (also called adaptor gestures) on anthropomorphic perceptions of five year old children. We developed and sorted a set of adaptor motions based on their intensity. We designed an experiment involving 20 children, in which they played a memory game with two robots. During moments of idleness, the first robot showed adaptor movements, while the second robot moved its head following basic face tracking. Results showed that the children perceived the robot displaying adaptor movements to be more human and friendly. Moreover, these traits were found to be proportional to the intensity of the adaptor movements. For the range of intensities tested, it was also found that adaptor movements were not disruptive towards the task. These findings corroborate the fact that adaptor movements improve the affective aspect of child-robot interactions (CRI) and do not interfere with the child’s performances in the task, making them suitable for CRI in educational contexts.

Posted on: September 19, 2017

Localization of emergency acoustic sources by micro aerial vehicles

  • Authors: Basiri, Meysam; Schill, Felix; Lima, Pedro; Floreano, Dario

For micro aerial vehicles (MAVs) involved in search and rescue missions, the ability to locate the source of a distress sound signal is significantly important and allows fast localization of victims and rescuers during nighttime, through foliage and in dust, fog, and smoke. Most emergency sound sources, such as safety whistles and personal alarms, generate a narrowband signal that is difficult to localize by human listeners or with the common localization methods suitable for broadband sounds. In this paper, we present three methods for MAV-based emergency sound localization system. The first method involves designing a new emergency source for immediate localization by the MAV using a common localization method. The other two novel methods allow localizing the currently available emergency sources, or other narrowband sounds in general, that are difficult to localize due to the periodicity in the sequence of sound samples. The second method exploits the Doppler shift in the sound frequency, caused due to the motion of the MAV and the dynamics of the MAV to assist with the localization. The third method involves active control of the robot’s attitude and fusing acoustic and attitude measurements for achieving accurate and robust estimates. We evaluate our methods in real-world experiments with real flying robots.

Posted on: July 12, 2017

One Constraint at a Time: Using Viability Principles in Integrative Modeling of Macromolecular Assemblies

  • Authors: Tamo, Giorgio E.; Maesani, Andrea; Traeger, Sylvain; Degiacomi, Matteo Thomas; Floreano, Dario; Dal Peraro, Matteo


Posted on: July 10, 2017

Brain-actuated gait trainer with visual and proprioceptive feedback

  • Authors: Liu, Dong; Chen, Weihai; Lee, Kyuhwa; Chavarriaga, Ricardo; Bouri, Mohamed; Pei, Zhongcai; Millán, José del R.

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension. Approach. We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects. Main Results. (i) For real-time classification, the average accuracy was 62.33 ± 4.95% and 63.89 ± 6.41% for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback (69.45 ± 9.95%) was significantly better than with visual feedback (62.89 ± 9.20%), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn. Significance. The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.

Posted on: July 10, 2017

Classification of Children"s Handwriting Errors for the Design of an Educational Co-writer Robotic Peer

  • Authors: Chandra, Shruti; Dillenbourg, Pierre; Paiva, Ana

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peer-tutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot’s handwriting using the {PT} method – A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot.

Posted on: June 30, 2017

Learning Externally Modulated Dynamical Systems

  • Authors: Sommer, Nicolas; Kronander, Klas; Billard, Aude

Dynamical Systems (DS) are often used to represent motion, with the advantage of being easy to learn from demonstrations. We present a method to modulate DS depending on an external signal, extending our previous work on Locally Modulated DS (LMDS, Kronander 2017). We present two applications of our system, which would not have been possible to achieve without taking external sensing into account in the DS motion formulation. The first application is a task of localization and grasping of objects, using our previous work on compliant tactile exploration. We successfully localize and grasp objects whose position is unknown, using touch in a simulated environment. In the second application, we teach a robot how to react to collisions in order to navigate between obstacles while reaching.

Posted on: June 30, 2017

Affect of Robot’s Competencies on Children’s Perception

  • Authors: Chandra, Shruti; Paradeda, Raul; Yin, Hang; Dillenbourg, Pierre; Prada, Rui; Paiva, Ana

The focus of the research described in this paper is to explore children’s perception of a social robot’s learning abilities and behavior in an educational context. With this purpose, we conducted a long-term study with children in a school by adopting the learning-by-teaching learning method. The scenario involves a ”learner-agent” (a robot) which seeks help from a child (a teacher) in correcting the shapes of a few letters it writes. Two versions of the robot were built: one where it learns and another where it does not improve over time. The results of the study suggest that children’s social relationship with the robot was not affected by the learning abilities of the agent.

Posted on: June 30, 2017