Reservoir computing, a machine learning framework used for modeling the brain, can predict temporal data with little observations and minimal computational resources. However, it is difficult to accurately reproduce the long-term target time series because the reservoir system becomes unstable. This predictive capability is required for a wide variety of time-series processing, including predictions of motor timing and chaotic dynamical systems. This study proposes oscillation-driven reservoir computing (ODRC) with feedback, where oscillatory signals are fed into a reservoir network to stabilize the network activity and induce complex reservoir dynamics. The ODRC can reproduce long-term target time series more accurately than conventional reservoir computing methods in a motor timing and chaotic time-series prediction tasks. Furthermore, it generates a time series similar to the target in the unexperienced period, that is, it can learn the abstract generative rules from limited observations. Given these significant improvements made by the simple and computationally inexpensive implementation, the ODRC would serve as a practical model of various time series data. Moreover, we will discuss biological implications of the ODRC, considering it as a model of neural oscillations and their cerebellar processors.

Affordances, a concept rooted in ecological psychology and pioneered by James J. Gibson, have emerged as a fundamental framework for understanding the dynamic relationship between individuals and their environments. Expanding beyond traditional perceptual and cognitive paradigms, affordances represent the inherent effect and action possibilities that objects offer to the agents within a given context. As a theoretical lens, affordances bridge the gap between effect and action, providing a nuanced understanding of the connections between agents' actions on entities and the effect of these actions. In this study, we propose a model that unifies object, action and effect into a single latent representation in a common latent space that is shared between all affordances that we call the affordance space. Using this affordance space, our system is able to generate effect trajectories when action and object are given and is able to generate action trajectories when effect trajectories and objects are given. In the experiments, we showed that our model does not learn the behavior of each object but it learns the affordance relations shared by the objects that we call equivalences. In addition to simulated experiments, we showed that our model can be used for direct imitation in real world cases. We also propose affordances as a base for Cross Embodiment transfer to link the actions of different robots. Finally, we introduce selective loss as a solution that allows valid outputs to be generated for indeterministic model inputs.

We observe a large variety of robots in terms of their bodies, sensors, and actuators. Given the commonalities in the skill sets, teaching each skill to each different robot independently is inefficient and not scalable when the large variety in the robotic landscape is considered. If we can learn the correspondences between the sensorimotor spaces of different robots, we can expect a skill that is learned in one robot can be more directly and easily transferred to other robots. In this paper, we propose a method to learn correspondences among two or more robots that may have different morphologies. To be specific, besides robots with similar morphologies with different degrees of freedom, we show that a fixed-based manipulator robot with joint control and a differential drive mobile robot can be addressed within the proposed framework. To set up the correspondence among the robots considered, an initial base task is demonstrated to the robots to achieve the same goal. Then, a common latent representation is learned along with the individual robot policies for achieving the goal. After the initial learning stage, the observation of a new task execution by one robot becomes sufficient to generate a latent space representation pertaining to the other robots to achieve the same task. We verified our system in a set of experiments where the correspondence between robots is learned (1) when the robots need to follow the same paths to achieve the same task, (2) when the robots need to follow different trajectories to achieve the same task, and (3) when complexities of the required sensorimotor trajectories are different for the robots. We also provide a proof-of-the-concept realization of correspondence learning between a real manipulator robot and a simulated mobile robot.

Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction. This method, however, requires an appropriate baseline for reliable determination of the contributions. We propose a compensated integrated gradients method that does not require a baseline. In fact, the method compensates the attributions calculated by integrated gradients at an arbitrary baseline using Shapley sampling. We prove that the method retrieves reliable attributions if the processes of input features in a classifier are mutually independent, and they are identical like shared weights in convolutional neural networks. Using three electroencephalogram datasets, we experimentally demonstrate that the attributions of the proposed method are more reliable than those of the original integrated gradients, and its computational complexity is much lower than that of Shapley sampling.

Monotonic policy improvement and off-policy learning are two main desirable properties for reinforcement learning algorithms. In this paper, by lower bounding the performance difference of two policies, we show that the monotonic policy improvement is guaranteed from on- and off-policy mixture samples. An optimization procedure which applies the proposed bound can be regarded as an off-policy natural policy gradient method. In order to support the theoretical result, we provide a trust region policy optimization method using experience replay as a naive application of our bound, and evaluate its performance in two classical benchmark problems.

To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present a non-constraint face-swapper based on 3D visual tracking that achieves real-time performance through parallel computing. Our imitator system is par- ticularly suited for experiments involving children with Autistic Spectrum Disorder who are often strongly disturbed by the constraints of other methods.

Frustration has been generally viewed in a negative light and its potential role in learning neglected. We propose a new approach to intrinsically motivated learning where frustration is a key factor that allows to dynamically balance exploration and exploitation. Moreover, based on the result obtained from our experiment with older infants, we propose that a temporary decrease in learning from negative feedback can also be beneficial in fine-tuning a newly learned behavior. We suggest that this temporal indifference to the outcome of an action may be related to the sense of control, and results from the state of elation, that is the experience of overcoming a very difficult task after prolonged frustration. Our preliminary simulation results serve as a proof-of-concept for our approach.

This article reports on the Sixth Robot World Cup Competition and Conference (RoboCup-2002) Fukuoka/Busan, which took place from 19 to 25 June in Fukuoka, Japan. It was the largest Robo- Cup since 1997 and held the first humanoid league competition in the world. Further, the first ROBOTREX (robot trade and exhibitions) was held with about 50 companies, universities, and institutes represented. To the best of our knowledge, this was the largest robotic event in history.

Competitions were held at Since the first competition in 1997 (Kitano Fukuoka Dome Baseball Stadium from 19 to 23 1998), RoboCup has grown into an international June followed by the International RoboCup joint research project in which about Symposium on 24 to 25 June. It is one of RoboCup is an attempt to foster intelligent the most ambitious projects of the twenty-first robotics research by providing a standard century. RoboCup currently consists of three problem, the ultimate goal of which is to divisions: (1) RoboCupSoccer, a move toward build a team of 11 humanoid robots that the final goal; (2) RoboCupRescue, a serious social can beat the human World Cup champion application of rescue activities for any kind soccer team by 2050. It's obvious that of disaster; and (3) RoboCupJunior, an international building a robot to play a soccer game is an education-based initiative designed to immense challenge; readers might therefore introduce young students to robotics. It is our intention to use since 1997 and showed its epoch-making new RoboCup as a vehicle to promote robotics standard for future RoboCups. One thousand and AI research by offering a publicly appealing four team members from 188 teams from 30 but formidable challenge (Asada et nations around the world participated. It included al. 1999; Kitano et al. 1997). The humanoid league is a big challenge knowledge, this was the largest robotic event with a long-term, high-impact goal, which in history.

RoboCup-2001 was the Fifth International RoboCup Competition and Conference. It was held for the first time in the United States, following RoboCup-2000 in Melbourne, Australia; RoboCup-99 in Stockholm; RoboCup-98 in Paris; and RoboCup-97 in Osaka.