60 research outputs found
Collaboration Development through Interactive Learning between Human and Robot
In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions
Dexterous hand-arm coordinated manipulation using active body-environment contact
Abstract-Human-symbiotic humanoid robots that can perform tasks dexterously using their hands are needed in our homes, welfare facilities, and other places. To improve their task performance, we propose a motion control scheme aimed at appropriately coordinated hand and arm motions. By observing human manual tasks, we identified active body-environment contact as a kind of human manual skill and devised a motion control scheme based on it. We also analyzed the effectiveness of active body-environment contact in glass-placing and drawer-opening tasks. We validated our motion control scheme through actual tests on a prototype human-symbiotic humanoid robot
Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks
We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones
FingerTac -- An Interchangeable and Wearable Tactile Sensor for the Fingertips of Human and Robot Hands
Skill transfer from humans to robots is challenging. Presently, many
researchers focus on capturing only position or joint angle data from humans to
teach the robots. Even though this approach has yielded impressive results for
grasping applications, reconstructing motion for object handling or fine
manipulation from a human hand to a robot hand has been sparsely explored.
Humans use tactile feedback to adjust their motion to various objects, but
capturing and reproducing the applied forces is an open research question. In
this paper we introduce a wearable fingertip tactile sensor, which captures the
distributed 3-axis force vectors on the fingertip. The fingertip tactile sensor
is interchangeable between the human hand and the robot hand, meaning that it
can also be assembled to fit on a robot hand such as the Allegro hand. This
paper presents the structural aspects of the sensor as well as the methodology
and approach used to design, manufacture, and calibrate the sensor. The sensor
is able to measure forces accurately with a mean absolute error of 0.21, 0.16,
and 0.44 Newtons in X, Y, and Z directions, respectively
Realtime Motion Generation with Active Perception Using Attention Mechanism for Cooking Robot
To support humans in their daily lives, robots are required to autonomously
learn, adapt to objects and environments, and perform the appropriate actions.
We tackled on the task of cooking scrambled eggs using real ingredients, in
which the robot needs to perceive the states of the egg and adjust stirring
movement in real time, while the egg is heated and the state changes
continuously. In previous works, handling changing objects was found to be
challenging because sensory information includes dynamical, both important or
noisy information, and the modality which should be focused on changes every
time, making it difficult to realize both perception and motion generation in
real time. We propose a predictive recurrent neural network with an attention
mechanism that can weigh the sensor input, distinguishing how important and
reliable each modality is, that realize quick and efficient perception and
motion generation. The model is trained with learning from the demonstration,
and allows the robot to acquire human-like skills. We validated the proposed
technique using the robot, Dry-AIREC, and with our learning model, it could
perform cooking eggs with unknown ingredients. The robot could change the
method of stirring and direction depending on the status of the egg, as in the
beginning it stirs in the whole pot, then subsequently, after the egg started
being heated, it starts flipping and splitting motion targeting specific areas,
although we did not explicitly indicate them
FOOTSTEP DETECTION AND CLASSIFICATION USING DISTRIBUTED MICROPHONES
ABSTRACT This paper addresses footstep detection and classification with multiple microphones distributed on the floor. We propose to introduce geometrical features such as position and velocity of a sound source for classification which is estimated by amplitude-based localization. It does not require precise inter-microphone time synchronization unlike a conventional microphone array technique. To classify various types of sound events, we introduce four types of features, i.e., time-domain, spectral and Cepstral features in addition to the geometrical features. We constructed a prototype system for footstep detection and classification based on the proposed ideas with eight microphones aligned in a 2-by-4 grid manner. Preliminary classification experiments showed that classification accuracy for four types of sound sources such as a walking footstep, running footstep, handclap, and utterance maintains over 70% even when the signal-to-noise ratio is low, like 0 dB. We also confirmed two advantages with the proposed footstep detection and classification. One is that the proposed features can be applied to classification of other sound sources besides footsteps. The other is that the use of a multichannel approach further improves noise-robustness by selecting the best microphone among the microphones, and providing geometrical information on a sound source
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