In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the rope was in constant flux during the operation of the robot. This required the robot control system to dynamically correspond to the state of the object at all times. However, a manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. To resolve this issue, we constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor, including visual and proximity sensors. The resultant model was verified to be capable of predicting the appropriate robot motions based on the sensory information available online. In addition, we designed certain task motions based on the Ian knot method using the dual-arm two-fingers robot. The designed knotting motions do not require a dedicated workbench or robot hand, thereby enhancing the versatility of the proposed method. Finally, experiments were performed to estimate the knotting performance of the real robot while executing overhand knots and bowknots on rope and its success rate. The experimental results established the effectiveness and high performance of the proposed method.

We propose an end-to-end learning method for autonomous driving systems in this article. End-to-end model estimates an appropriate motor command from raw sensory signals. End-to-end model for autonomous driving systems has recently been based on neural networks, which are popular for their good recognition ability. A common problem is how to return a car to the driving lane when the car goes off the track. In our research, we collect recovery data based on the distance from a desired track (the nearest waypoint link) during a road test with a simulator. To train the recovery behavior, instead of collecting human driving data, we use a path-following module (which means the car automatically drives on a pre-decided route using the car's current position). Our proposed method is divided into three phases. In phase 1, we collect data only using a path-following module during 100 laps of driving. In phase 2, we generate driving behavior using a neural driving module trained by the data collected in phase 1. This includes switching between the accelerator, brake and steering based on a threshold. We collect further data on the recovery behavior using the path-following module during 100 laps of driving. In phase 3, we generate driving behavior using the neural driving module trained by the data collected in phases 1 and 2. To assess the proposed method, we compared the average distance from the nearest waypoint link and the average distance traveled per lap for datasets with no recovery, for datasets with random recovery, and for datasets for the proposed method with recovery. A model based on the proposed method drove well and paid more attention to the road rather than the sky and other unrelated objects automatically for both untrained and trained courses and weather.

This paper presents a novel computational approach for modeling and generating multiple object manipulation behaviors by a humanoid robot. The contribution of this paper is that deep learning methods are applied not only for multimodal sensor fusion but also for sensory-motor coordination. More specifically, a time-delay deep neural network is applied for modeling multiple behavior patterns represented with multi-dimensional visuomotor temporal sequences. By using the efficient training performance of Hessian-free optimization, the proposed mechanism successfully models six different object manipulation behaviors in a single network. The generalization capability of the learning mechanism enables the acquired model to perform the functions of cross-modal memory retrieval and temporal sequence prediction. The experimental results show that the motion patterns for object manipulation behaviors are successfully generated from the corresponding image sequence, and vice versa. Moreover, the temporal sequence prediction enables the robot to interactively switch multiple behaviors in accordance with changes in the displayed objects.

Our brain is known to enhance perceptual precision and reduce ambiguity about sensory environment by integrating multiple sources of sensory information acquired from different modalities, such as vision, auditory and somatic sensation. From an engineering perspective, building a computational model that replicates this ability to integrate multimodal information and to self-organize the causal dependency among them, represents one of the central challenges in robotics. In this study, we propose such a model based on a deep learning framework and we evaluate the proposed model by conducting a bell ring task using a small humanoid robot. Our experimental results demonstrate that (1) the cross-modal memory retrieval function of the proposed method succeeds in generating visual sequence from the corresponding sound and bell ring motion, and (2) the proposed method leads to accurate causal dependencies among the sensory-motor sequence.

Safety issue has become the primary concern in wheelchair mounted robotic arm applications. We introduced mechanical gravity canceller in the design to realize a light weight manipulator, which also yield the greatly simplify of manipulator dynamics. Based on the simplified dynamics, sensor based contact detection can be easily implemented to enable safety. Contact reaction schemes are also applied through a impedance control law to achieve desired backdrivability. Experiments are conducted to illustrate the proposed method.

We created a human-robot communication system that can adapt to user preferences that can easily change through communication. Even if any learning algorithms are used, evaluating the human-robot interaction is indispensable and difficult. To solve this problem, we installed a machine learning algorithm called interactive evolutionary computation (IEC) into a communication robot named WAMOEBA-3. IEC is a kind of evolutionary computation like a genetic algorithm. With IEC, the fitness function is performed by each user. We carried out experiments on the communication learning system using an advanced IEC system named HMHE. Before the experiments, we did not tell the subjects anything about the robot, so the interaction differed among the experimental subjects. We could observe mutual adaptation, because some subjects noticed the robot's functions and changed their interaction. From the results, we confirmed that, in spite of the changes of the preferences, the system can adapt to the interaction of multiple users

This paper describes how to implement interactive evolutionary computation (IEC) into a human-robot communication system. IEC is an evolutionary computation (EC) in which the fitness function is performed by human assessors. We used IEC to configure the human-robot communication system. We have already simulated IEC's application. In this paper, we implemented IEC into a real robot. Since this experiment leads considerable burdens on both the robot and experimental subjects, we propose the human-machine hybrid evaluation (HMHE) to increase the diversity within the genetic pool without increasing the number of interactions. We used a communication robot, WAMOEBA-3 (Waseda artificial mind on emotion base), which is appropriate for this experiment. In the experiment, human assessors interacted with WAMOEBA-3 in various ways. The fitness values increased gradually, and assessors felt the robot learnt the motions they desired. Therefore, it was confirmed that the IEC is most suitable as the communication learning system.

We developed an emotional communication robot, WAMOEBA, using behavior-based techniques. We also proposed motor-agent (MA) model, which is an autonomous distributed-control algorithm constructed of simple sensor motor coordination. Though it enables WAMOEBA to behave in various ways, the weight of the combinations between different motor agents is influenced by the preferences of the developer. We usually use machine-learning algorithms to automatically configure these parameters for communication robots. However, this makes it difficult to define the quantitative evaluation required for communication. We therefore used the method of interactive evolutionary computation (IEC), which can be applied to problems involving quantitative evaluation. IEC does not require to define a fitness function; this task is performed by users. But the biggest problem with using IEC is human fatigue, which causes insufficiency of individuals and generations for convergence of EC. To fix this problem, we use the prediction function that automatically calculates the fitness values of genes from some samples that have received the human subjective evaluation. Then, we carried out the behavior acquisition experiment using the IEC simulation system with the prediction function. As the results of experiments, it is confirmed that diversifying the genetic pool is an efficient way for generating a variety of behavior.

Recent studies on embodied cognitive science have shown us the possibility of emergence of more complex and nontrivial behaviors with quite simple designs if the designer takes the dynamics of the system-environment interaction into account properly. In this paper, we report our tentative classification experiments of several objects using the human-like autonomous robot, "WAMOEBA-2Ri". As modeling the environment, we focus on not only static aspects of the environment but also dynamic aspects of it including that of the system own. The visualized result of this experiment shows the integration of multimodal sensor dataset acquired by the system-environment interaction ("grasping") enable robust categorization of several objects. Finally, in discussion, we demonstrate a possible application to making "invariance in motion" emerge consequently by extending this approach.