A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Chapter RoCKIn@Home: Domestic Robots Challenge

Service robots performing complex tasks involving people in houses or public environments are becoming more and more common, and there is a huge interest from both the research and the industrial point of view. The RoCKIn@Home challenge has been designed to compare and evaluate different approaches and solutions to tasks related to the development of domestic and service robots. RoCKIn@Home competitions have been designed and executed according to the benchmarking methodology developed during the project and received very positive feedbacks from the participating teams. Tasks and functionality benchmarks are explained in detail

RoCKIn@Home: Domestic Robots Challenge

Service robots performing complex tasks involving people in houses or public environments are becoming more and more common, and there is a huge interest from both the research and the industrial point of view. The RoCKIn@Home challenge has been designed to compare and evaluate different approaches and solutions to tasks related to the development of domestic and service robots. RoCKIn@Home competitions have been designed and executed according to the benchmarking methodology developed during the project and received very positive feedbacks from the participating teams. Tasks and functionality benchmarks are explained in detail

Interactive generation and learning of semantic-driven robot behaviors

The generation of adaptive and reflexive behavior is a challenging task in artificial intelligence and robotics. In this thesis, we develop a framework for knowledge representation, acquisition, and behavior generation that explicitly incorporates semantics, adaptive reasoning and knowledge revision. By using our model, semantic information can be exploited by traditional planning and decision making frameworks to generate empirically effective and adaptive robot behaviors, as well as to enable complex but natural human-robot interactions. In our work, we introduce a model of semantic mapping, we connect it with the notion of affordances, and we use those concepts to develop semantic-driven algorithms for knowledge acquisition, update, learning and robot behavior generation. In particular, we apply such models within existing planning and decision making frameworks to achieve semantic-driven and adaptive robot behaviors in a generic environment. On the one hand, this work generalizes existing semantic mapping models and extends them to include the notion of affordances. On the other hand, this work integrates semantic information within well-defined long-term planning and situated action frameworks to effectively generate adaptive robot behaviors. We validate our approach by evaluating it on a number of problems and robot tasks. In particular, we consider service robots deployed in interactive and social domains, such as offices and domestic environments. To this end, we also develop prototype applications that are useful for evaluation purposes

Spatial representation for planning and executing robot behaviors in complex environments

Robots are already improving our well-being and productivity in different applications such as industry, health-care and indoor service applications. However, we are still far from developing (and releasing) a fully functional robotic agent that can autonomously survive in tasks that require human-level cognitive capabilities. Robotic systems on the market, in fact, are designed to address specific applications, and can only run pre-defined behaviors to robustly repeat few tasks (e.g., assembling objects parts, vacuum cleaning). They internal representation of the world is usually constrained to the task they are performing, and does not allows for generalization to other scenarios. Unfortunately, such a paradigm only apply to a very limited set of domains, where the environment can be assumed to be static, and its dynamics can be handled before deployment. Additionally, robots configured in this way will eventually fail if their "handcrafted'' representation of the environment does not match the external world. Hence, to enable more sophisticated cognitive skills, we investigate how to design robots to properly represent the environment and behave accordingly. To this end, we formalize a representation of the environment that enhances the robot spatial knowledge to explicitly include a representation of its own actions. Spatial knowledge constitutes the core of the robot understanding of the environment, however it is not sufficient to represent what the robot is capable to do in it. To overcome such a limitation, we formalize SK4R, a spatial knowledge representation for robots which enhances spatial knowledge with a novel and "functional" point of view that explicitly models robot actions. To this end, we exploit the concept of affordances, introduced to express opportunities (actions) that objects offer to an agent. To encode affordances within SK4R, we define the "affordance semantics" of actions that is used to annotate an environment, and to represent to which extent robot actions support goal-oriented behaviors. We demonstrate the benefits of a functional representation of the environment in multiple robotic scenarios that traverse and contribute different research topics relating to: robot knowledge representations, social robotics, multi-robot systems and robot learning and planning. We show how a domain-specific representation, that explicitly encodes affordance semantics, provides the robot with a more concrete understanding of the environment and of the effects that its actions have on it. The goal of our work is to design an agent that will no longer execute an action, because of mere pre-defined routine, rather, it will execute an actions because it "knows'' that the resulting state leads one step closer to success in its task