Roberto Capobianco | Università degli Studi "La Sapienza" di Roma (original) (raw)
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Papers by Roberto Capobianco
Affordances have been introduced in literature as action opportunities that objects offer, and us... more Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances (STA) and Spatio-Temporal Affordance Map (STAM). Using this formalism, we encode action semantics related to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that affordances encode accurate semantics of the environment.
RoboCup soccer competitions are considered among the most challenging multi-robot adversarial env... more RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved.
Robots, in order to properly interact with people and effectively perform the requested tasks, sh... more 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.
In the last years, Learning by Imitation (LbI) has been increasingly explored in order to easily ... more In the last years, Learning by Imitation (LbI) has been increasingly explored in order to easily instruct robots to execute complex motion tasks. However, most of the approaches do not consider the case in which multiple and sometimes conflicting demonstrations are given by different teachers. Nevertheless, it seems advisable that the robot does not start as a tabula-rasa, but re-using previous knowledge in imitation learning is still a difficult research problem. In order to be used in real applications, LbI techniques should be robust and incremental. For this reason, the challenge of our research is to find alternative methods for incremental, multi-teacher LbI.
Lecture Notes in Computer Science, 2015
Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e.,... more 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.
Robots need a suitable representation of the surrounding world to operate in a structured but dyn... more Robots need a suitable representation of the surrounding world to operate in a structured but dynamic environment. State-of-theart 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.
The representation of the knowledge needed by a robot to perform complex tasks is restricted by t... more The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.
2013 16th International Conference on Advanced Robotics (ICAR), 2013
Human Robot Interaction is a key enabling feature to support the introduction of robots in everyd... more 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.
Thesis Chapters by Roberto Capobianco
The generation of adaptive and reflexive behavior is a challenging task in artificial intelligenc... more 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.
Affordances have been introduced in literature as action opportunities that objects offer, and us... more Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances (STA) and Spatio-Temporal Affordance Map (STAM). Using this formalism, we encode action semantics related to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that affordances encode accurate semantics of the environment.
RoboCup soccer competitions are considered among the most challenging multi-robot adversarial env... more RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved.
Robots, in order to properly interact with people and effectively perform the requested tasks, sh... more 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.
In the last years, Learning by Imitation (LbI) has been increasingly explored in order to easily ... more In the last years, Learning by Imitation (LbI) has been increasingly explored in order to easily instruct robots to execute complex motion tasks. However, most of the approaches do not consider the case in which multiple and sometimes conflicting demonstrations are given by different teachers. Nevertheless, it seems advisable that the robot does not start as a tabula-rasa, but re-using previous knowledge in imitation learning is still a difficult research problem. In order to be used in real applications, LbI techniques should be robust and incremental. For this reason, the challenge of our research is to find alternative methods for incremental, multi-teacher LbI.
Lecture Notes in Computer Science, 2015
Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e.,... more 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.
Robots need a suitable representation of the surrounding world to operate in a structured but dyn... more Robots need a suitable representation of the surrounding world to operate in a structured but dynamic environment. State-of-theart 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.
The representation of the knowledge needed by a robot to perform complex tasks is restricted by t... more The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.
2013 16th International Conference on Advanced Robotics (ICAR), 2013
Human Robot Interaction is a key enabling feature to support the introduction of robots in everyd... more 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.
The generation of adaptive and reflexive behavior is a challenging task in artificial intelligenc... more 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.