Linear temporal logic (LTL) synthesis is a formal method for automatically composing a reactive system that realizes a given behavioral specification described in LTL if the specification is realizable. Even if the whole specification is unrealizable, it is preferable to synthesize a best-effort reactive system. That is, a system that maximally realizes its partial specifications. Therefore, we categorized specifications into must specifications (which should never be violated) and desirable specifications (the violation of which may be unavoidable). In this paper, we propose a method for synthesizing a reactive system that realizes all must specifications and strongly endeavors to satisfy each desirable specification. The general form of the desirable specifications without assumptions is $$\mathbf{G }\varphi $$ , which means “ $$\varphi $$ always holds”. In our approach, the best effort to satisfy $$\mathbf{G }\varphi $$ is to maximize the number of steps satisfying $$\varphi $$ in the interaction. To quantitatively evaluate the number of steps, we used a mean-payoff objective based on LTL formulae. Our method applies the Safraless approach to construct safety games from given must and desirable specifications, where the must specification can be written in full LTL and may include assumptions. It then transforms the safety games constructed from the desirable specifications into mean-payoff games and finally composes a reactive system as an optimal strategy on a synchronized product of the games.

This paper presents an anthropomorphic robot hand called Osaka-City-University-Hand II, which is an improved version of Osaka-City-University-Hand I. The cosmetic and the function of our proposed hand allow us to use the hand as a prosthetic hand in addition to as an open platform of robot hands for robotics research. Distributed tactile and force sensors are appended to the OCU-Hand II as a feedback system in order to grasp an object firmly. A new control strategy is to be adopted, in which a master light-weight glove is to be used to drive the OCU-Hand II as a slave. In the tele-operating task, arithmetic operations are to be done to the outputs of the feedback sensors in order to increase the resolution of the master-slave driving technique as well as to overcome the hardware amplification defects. In order to use OCU-Hand II as a helper equipment, a novel and unique assistive mode is included within master-slave control strategy, where the OCU-Hand II is assisting and helping its operators in order to reduce the load on the operator and perform the usual operations in a better manner or even faster than the usual. During the assistive mode the operator is enabled to perform different tasks other than the master-slave driving while he is still putting on the master-glove.

In this paper, we are concerned with the problem of how a physical robot can get an appropriate internal representation to its task and environment. Learning from experience is effective for the problem, but it is very time-consuming to learn a representation from the beginning in a real environment. On the other hand, the representation learned only in a simulated environment has the risk of not serving the purpose in a real environment because of the uncertainty in sensors, actuators, and the environment. In, order to have the best of both worlds, it is effective to transplant the learned state representation of a virtual agent to a physical robot. For this purpose., we improved our developed incremental learning architecture for use in the real environment and developed a new architecture, called STNS-R. In this architecture, inappropriate negative instances caused by uncertainties are found on the basis of the distribution of instances and removed in order to correct the distorted shapes of the states. The effectiveness of STNS-R is shown in the experimental results.

We discuss roles of robots as autonomous knowledge media and show our prototype system of an office work assistant robot based on this approach. We are surrounded by a huge amount of artifacts and information that is difficult for humans to deal with. Robots can help people by gathering and arranging information intelligently instead of people themselves. Our prototype system called Kappa III is an office work assistant robot that can tell people the location of daily goods in the office. It firstly looks around to capture images of desks in order to remember what and where such goods are. Then it can identify them by cutting them out from the background and categorizing them by color, shape, and figure. People can ask it to take goods in the office by either specifying names or features such as color. We also realized discovery of objects three-dimensionally by comparing captured scenes with expected ones.

Reinforcement learning is very useful for robots with little a priori knowledge in acquiring appropriate behavior. This paper describes a learning system which can learn a state representation and a behavior policy simultaneously while executing the task. We call the system - the situation transition network system. As cognitive learning, it extracts "situations" and maintains them dynamically in the continuous state space on the basis of rewards from the environment. As behavior learning, it leads to a Markov decision model of environment and performs partial planning on the model. This is a kind of reinforcement learning. The results of computer simulations are given.

This paper describes a system with which a cognitive agent learns the way of abstraction and the policy of behavior selection simultaneously. We call the system situation transition network system (STNS). The system extracts situations and maintains them dynamically in the continuous state space on the basis of rewards from the environment. In this way, the system learns the way of abstraction in a dynamic environment. At the same time, the system stores results of transitions between situations and constructs a network of situations. This network is used for partial planning. At a point of time in the learning process, the system selects a behavior according to the partial plan. Because the planning is performed on a network of the abstracted situations, the agent with STNS does not have to deliberate details in planning. Furthermore, the agent can make a plan even on the early stage of learning because the planning is partial. Owing to the simultaneous learning with task executions the agent can adapt to the current task. The results of computer simulations are given.