Guglielmo Gemignani

On-line semantic mapping

Human Robot Interaction is a key enabling feature to support the introduction of robots in everyday environments. However, robots are currently incapable of building representations of the environments that allow both for the execution of complex tasks and for an easy interaction with the user requesting them. In this paper, we focus on semantic mapping, namely the problem of building a representation of the environment that combines metric and symbolic information about the elements of the environment and the objects therein. Specifically, we extend previous approaches, by enabling on-line semantic mapping, that permits to add to the representation elements acquired through a long term interaction with the user. The proposed approach has been experimentally validated on different kinds of environments, several users, and multiple robotic platforms.

Automatic Extraction of Structural Representations of Environments

Robots need a suitable representation of the surrounding world to operate in a structured but dynamic environment. State-of-the-art approaches usually rely on a combination of metric and topological maps and require an expert to provide the knowledge to the robot in a suitable format. Therefore, additional symbolic knowledge cannot be easily added to the representation in an incremental manner. This work deals with the problem of effectively binding together the high-level semantic information with the low-level knowledge represented in the metric map by introducing an intermediate grid-based representation. In order to demonstrate its effectiveness, the proposed approach has been experimentally validated on different kinds of environments.

Living with robots

Robots, in order to properly interact with people and effectively perform the requested tasks, should have a deep and specific knowledge of the environment they live in. Current capabilities of robotic platforms in understanding the surrounding environment and the assigned tasks are limited, despite the recent progress in robotic perception. Moreover, novel improvements in human-robot interaction support the view that robots should be regarded as intelligent agents that can request the help of the user to improve their knowledge and performance.In this paper, we present a novel approach to semantic mapping. Instead of requiring our robots to autonomously learn every possible aspect of the environment, we propose a shift in perspective, allowing non-expert users to shape robot knowledge through human-robot interaction. Thus, we present a fully operational prototype system that is able to incrementally and on-line build a rich and specific representation of the environment. Such a novel representation combines the metric information needed for navigation tasks with the symbolic information that conveys meaning to the elements of the environment and the objects therein. Thanks to such a representation, we are able to exploit multiple AI techniques to solve spatial referring expressions and support task execution. The proposed approach has been experimentally validated on different kinds of environments, by several users, and on multiple robotic platforms. A method for incremental and on-line semantic mapping based on HRI.A four-layered representation for semantic maps used to support robot task execution.Throughout description and evaluation of a fully semantic mapping system.

Language-Based Sensing Descriptors for Robot Object Grounding

In this work, we consider an autonomous robot that is required to understand commands given by a human through natural language. Specifically, we assume that this robot is provided with an internal representation of the environment. However, such a representation is unknown to the user. In this context, we address the problem of allowing a human to understand the robot internal representation through dialog. To this end, we introduce the concept of sensing descriptors. Such representations are used by the robot to recognize unknown object properties in the given commands and warn the user about them. Additionally, we show how these properties can be learned over time by leveraging past interactions in order to enhance the grounding capabilities of the robot.

Interactive Semantic Mapping: Experimental Evaluation

Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping.

Disambiguating localization symmetry through a Multi-Clustered Particle Filtering

Distributed Particle filter-based algorithms have been proven effective tools to model non-linear and dynamic processes in Multi Robot Systems. In complex scenarios, where mobile agents are involved, it is crucial to disseminate reliable beliefs among agents to avoid the degradation of the global estimations.We present a cluster-based data association to boost the performance of a Distributed Particle Filter. Exploiting such data association, we propose a disambiguation method for the RoboCup scenario robust to noise and false perceptions. The results obtained using both a simulated and a real environment demonstrate the effectiveness of the proposed approach.

Multi-robot search for a moving target: Integrating world modeling, task assignment and context

In this paper, we address coordination within a team of cooperative autonomous robots that need to accomplish a common goal. Our survey of the vast literature on the subject highlights two directions to further improve the performance of a multi-robot team. In particular, in a dynamic environment, coordination needs to be adapted to the different situations at hand (for example, when there is a dramatic loss of performance due to unreliable communication network). To this end, we contribute a novel approach for coordinating robots. Such an approach allows a robotic team to exploit environmental knowledge to adapt to various circumstances encountered, enhancing its overall performance. This result is achieved by dynamically adapting the underlying task assignment and distributed world representation, based on the current state of the environment. We demonstrate the effectiveness of our coordination system by applying it to the problem of locating a moving, non-adversarial target. In particular, we report on experiments carried out with a team of humanoid robots in a soccer scenario and a team of mobile bases in an office environment.

Approaching Qualitative Spatial Reasoning About Distances and Directions in Robotics

One of the long-term goals of our society is to build robots able to live side by side with humans. In order to do so, robots need to be able to reason in a qualitative way. To this end, over the last years, the Artificial Intelligence research community has developed a considerable amount of qualitative reasoners. The majority of such approaches, however, has been developed under the assumption that suitable representations of the world were available. In this paper, we propose a method for performing qualitative spatial reasoning in robotics on abstract representations of environments, automatically extracted from metric maps. Both the representation and the reasoner are used to perform the grounding of commands vocally given by the user. The approach has been verified on a real robot interacting with several non-expert users.

Graph-Based Task Libraries for Robots: Generalization and Autocompletion

In this paper, we consider an autonomous robot that persists over time performing tasks and the problem of providing one additional task to the robot’s task library. We present an approach to generalize tasks, represented as parameterized graphs with sequences, conditionals, and looping constructs of sensing and actuation primitives. Our approach performs graph-structure task generalization, while maintaining task executability and parameter value distributions. We present an algorithm that, given the initial steps of a new task, proposes an autocompletion based on a recognized past similar task. Our generalization and autocompletion contributions are effective on different real robots. We show concrete examples of the robot primitives and task graphs, as well as results, with Baxter. In experiments with multiple tasks, we show a significant reduction in the number of new task steps to be provided.

Dialogue with Robots to Support Symbiotic Autonomy

Service Robotics is finding solutions to enable effective interaction with users. Among the several issues, the need of adapting robots to the way humans usually communicate is becoming a key and challenging task. In this context the design of robots that understand and reply in Natural Language plays a central role, especially when interactions involve untrained users. In particular, this is even more stressed in the framework of Symbiotic Autonomy, where an interaction is always required for the robot to accomplish a given task. In this article, we propose a framework to model dialogues with robotic platforms, enabling effective and natural dialogic interactions. The framework relies on well-known theories as well as on perceptually informed spoken language understanding processors, giving rise to interactions that are tightly bound to the operating scenario.