We present a new framework for controlling a robot collaborating with a human to accomplish a common mission. Knowing that we are interested in collaboration domains where there is no shared plan between the human and the robot, the constraints on the decision process are more challenging. We study the decision process of a robot agent for a specific shared mission with a human considering the effect of the human presence, the planning flexibility according to human comfortability and achieving mission. We choose to formalize this problem with Partially Observable Markov Decision Process, then we describe a new domain example that represent human-robot collaboration with no shared plan and we show some preliminary results of solving the POMDP model with standard optimal algorithms as a base work to compare with state-of-the-art and future-work approximate algorithms.

Due to the decrease of sensor and actuator prices and their ease of installation, smart homes and smart environments are more and more exploited in automation and health applications. In these applications, activity recognition has an important place. This article presents a general architecture that is responsible for adapting automation for the different users of the smart home while recognizing their activities. For that, semi-supervised learning algorithms and Markov-based models are used to determine the preferences of the user considering a combination of: (1) observations of the data that have been acquired since the start of the experiment and (2) feedback of the users on decisions that have been taken by the automation. We present preliminarily simulated experimental results regarding the determination of preferences for a user.

We are interested in collaboration domains between a robot and a human partner, the partners share a common mission without an explicit communication about their plans. The decision process of the robot agent should consider the presence of its human partner. Also, the robot planning should be flexible to human comfortability and all possible changes in the shared environment. To solve the problem of human-robot collaboration with no communication, we present a model that gives the robot the ability to build a belief over human intentions in order to predict his goals, this model counts mainly on observing the human actions. We integrate this prediction into a Partially Observable Markov Decision Process (POMDP) model to achieve the most appropriate and flexible decisions for the robot.

Integrated networks of mobile robots, personal smart devices, and smart spaces can provide for a more accurate data for user assistance than if the former are used individually. We call this network of personal and smart space devices and robots "Robots-Assisted Ambient Intelligence" (RAmI). Additionally, with the application of distributed network optimization, not only can we improve the assistance of an individual user, but we can also minimize conflict or congestion created when multiple users in large installations use the limited resources of RAmI that are spatially and temporally constrained. The emphasis of RAmI is on the efficiency and effectiveness of multiple and simultaneous user assistance and on the influence of individual robot actions on the desired system's performance. In this paper, we model RAmI as a multi-agent system with AmI and robot agents. Moreover, we propose a modular three-layer architecture for each robot agent and discuss its application and communication requirements with the emphasis on interaction between robots, humans, and AmI agents to facilitate efficient usage of limited RAmI resources. Our approach is showcased by means of a case study where we focus on meal and medicine delivery to patients in large hospitals.