Dealing with sparse rewards is one of the most important challenges in reinforcement
learning (RL), especially when a goal is dynamic (eg, to grasp a moving object). Hindsight …

Smart home environments have a significant potential to provide for long-term monitoring of
users with special needs in order to promote the possibility to age at home. Such …

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in
everyday environments, where they cooperate to perform complex tasks. While their …

Robotic ecologies are systems made out of several robotic devices, including mobile robots,
wireless sensors and effectors embedded in everyday environments, where they cooperate …

Enabling robots to seamlessly operate as part of smart spaces is an important and extended
challenge for robotics R&D and a key enabler for a range of advanced robotic applications …

We present an approach to make planning adaptive in order to enable context-aware mobile
robot navigation. We integrate a model-based planner with a distributed learning system …

Robots operating in the presence of human should adapt their plans and behavior
accordingly. The recent area of humanaware planning (HAP) addresses this problem. In this …

In heterogeneous multi-robot teams, robustness and flexibility are increased by the diversity
of the robots, each contributing different capabilities. Yet platform-independence is desirable …

Smart robotic environments combine traditional (ambient) sensing devices and mobile
robots. This combination extends the type of applications that can be considered, reduces …

The paper presents a simple people-centric adaptive ecology between a humanoid robot
and a virtual human (social agents) to perform a real-world common complex social task …