Daniele Nardi

ICRA Conference 2024 Conference Paper

AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture

• Leonardo Saraceni
• Ionut Marian Motoi
• Daniele Nardi
• Thomas A. Ciarfuglia

The problem of multi-object tracking (MOT) consists in detecting and tracking all the objects in a video sequence while keeping a unique identifier for each object. It is a challenging and fundamental problem for robotics. In precision agriculture the challenge of achieving a satisfactory solution is amplified by extreme camera motion, sudden illumination changes, and strong occlusions. Most modern trackers rely on the appearance of objects rather than motion for association, which can be ineffective when most targets are static objects with the same appearance, as in the agricultural case. To this end, on the trail of SORT [5], we propose AgriSORT, a simple, online, real-time tracking-by-detection pipeline for precision agriculture based only on motion information that allows for accurate and fast propagation of tracks between frames. The main focuses of AgriSORT are efficiency, flexibility, minimal dependencies, and ease of deployment on robotic platforms. We test the proposed pipeline on a novel MOT benchmark specifically tailored for the agricultural context, based on video sequences taken in a table grape vineyard, particularly challenging due to strong self-similarity and density of the instances. Both the code and the dataset are available for future comparisons at: https://github.com/Lio320/AgriSORT

IROS Conference 2024 Conference Paper

EMPOWER: Embodied Multi-role Open-vocabulary Planning with Online Grounding and Execution

• Francesco Argenziano
• Michele Brienza
• Vincenzo Suriani
• Daniele Nardi
• Domenico Daniele Bloisi

Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the challenge of maintaining low computational overhead given the limited resources of robotic hardware. We introduce EMPOWER, a framework designed for open-vocabulary online grounding and planning for embodied agents aimed at addressing these issues. By leveraging efficient pre-trained foundation models and a multi-role mechanism, EMPOWER demonstrates notable improvements in grounded planning and execution. Quantitative results highlight the effectiveness of our approach, achieving an average success rate of 0. 73 across six different real-life scenarios using a TIAGo robot.

ECAI Conference 2024 Conference Paper

Multi-Agent Planning Using Visual Language Models

• Michele Brienza
• Francesco Argenziano
• Vincenzo Suriani
• Domenico Daniele Bloisi
• Daniele Nardi

Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tasks. However, LLMs and VLMs can produce erroneous results, especially when a deep understanding of the problem domain is required. For instance, when planning and perception are needed simultaneously, these models often struggle because of difficulties in merging multi-modal information. To address this issue, fine-tuned models are typically employed and trained on specialized data structures representing the environment. This approach has limited effectiveness, as it can overly complicate the context for processing. In this paper, we propose a multi-agent architecture for embodied task planning that operates without the need for specific data structures as input. Instead, it uses a single image of the environment, handling free-form domains by leveraging commonsense knowledge. We also introduce a novel, fully automatic evaluation procedure, PG2S, designed to better assess the quality of a plan. We validated our approach using the widely recognized ALFRED dataset, comparing PG2S to the existing KAS metric to further evaluate the quality of the generated plans.

JAIR Journal 2023 Journal Article

An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains

• Jim Martin Catacora Ocana
• Roberto Capobianco
• Daniele Nardi

Deep reinforcement learning has achieved impressive results in recent years; yet, it is still severely troubled by environments showcasing sparse rewards. On top of that, not all sparse-reward environments are created equal, i.e., they can differ in the presence or absence of various features, with many of them having a great impact on learning. In light of this, the present work puts together a literature compilation of such environmental features, covering particularly those that have been taken advantage of and those that continue to pose a challenge. We expect this effort to provide guidance to researchers for assessing the generality of their new proposals and to call their attention to issues that remain unresolved when dealing with sparse rewards.

IROS Conference 2022 Conference Paper

Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints

• Akshay Dhonthi
• Philipp Schillinger
• Leonel Rozo
• Daniele Nardi

For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories exist, being learning and optimization the main driving techniques. Our work builds on the learning-from-demonstration (LfD) paradigm, where an expert demonstrates motions, and the robot learns to imitate them. However, expert demonstrations are not sufficient to capture all sorts of task specifications, such as the timing to grasp an object. In this paper, we propose a new method that considers formal task specifications within LfD skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form of temporal properties of systems, to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate our approach in simulation and on a real industrial setting using several tasks that showcase how our approach addresses the LfD limitations using STL and BBO.

PRL Workshop 2021 Workshop Paper

Learning a Symbolic Planning Domain through the Interaction with Continuous Environments

• Elena Umili
• Emanuele Antonioni
• Francesco Riccio
• Roberto Capobianco
• Daniele Nardi
• Giuseppe De Giacomo

One of the main challenges in AI is performing dynamic tasks by using approaches that efficiently predict the environment’s future outcomes. State-of-the-art planners can reason effectively with symbolic representations of the environment. However, when the environment is continuous and unstructured, manually extracting an ad-hoc symbolic model to perform planning may be infeasible. Deep Reinforcement Learning is known to automatically learn compact representations of the state space through interaction with the environment. However, it is not suitable for planning, giving up the efficiency we would gain by predicting the consequences of actions. This work focuses on continuous state-space MDPs and proposes an approach that naturally combines interaction, symbolic representation learning, and symbolic online planning. Our system leverages experience-data gained from the environment to autonomously learn a symbolic planning model composed of: (1) a symbol grounding model to switch from continuous to symbolic space and vice versa; (2) a symbolic transition model; (3) a value function for symbolic states. This model is used at training time to lead the interaction with the world. At each interaction step, we perform fast symbolic online planning over a finite horizon to choose the action to execute in the environment. The success of this strategy in the environment implicitly validates our automatically extracted symbolic model, since the system is able to effectively plan actions in the original MDP by reasoning only in the finite and symbolic domain. The approach has been evaluated on several continual OpenAI gym environments, addressing successfully both control problems and games.

IJCAI Conference 2020 Conference Paper

Explainable Inference on Sequential Data via Memory-Tracking

• Biagio La Rosa
• Roberto Capobianco
• Daniele Nardi

In this paper we present a novel mechanism to get explanations that allow to better understand network predictions when dealing with sequential data. Specifically, we adopt memory-based networks — Differential Neural Computers — to exploit their capability of storing data in memory and reusing it for inference. By tracking both the memory access at prediction time, and the information stored by the network at each step of the input sequence, we can retrieve the most relevant input steps associated to each prediction. We validate our approach (1) on a modified T-maze, which is a non-Markovian discrete control task evaluating an algorithm’s ability to correlate events far apart in history, and (2) on the Story Cloze Test, which is a commonsense reasoning framework for evaluating story understanding that requires a system to choose the correct ending to a four-sentence story. Our results show that we are able to explain agent’s decisions in (1) and to reconstruct the most relevant sentences used by the network to select the story ending in (2). Additionally, we show not only that by removing those sentences the network prediction changes, but also that the same are sufficient to reproduce the inference.

AIJ Journal 2020 Journal Article

Grounded language interpretation of robotic commands through structured learning

• Andrea Vanzo
• Danilo Croce
• Emanuele Bastianelli
• Roberto Basili
• Daniele Nardi

The presence of robots in everyday life is increasing day by day at a growing pace. Industrial and working environments, health-care assistance in public or domestic areas can benefit from robots' services to accomplish manifold tasks that are difficult and annoying for humans. In such scenarios, Natural Language interactions, enabling collaboration and robot control, are meant to be situated, in the sense that both the user and the robot access and make reference to the environment. Contextual knowledge may thus play a key role in solving inherent ambiguities of grounded language as, for example, the prepositional phrase attachment. In this work, we present a linguistic pipeline for semantic processing of robotic commands, that combines discriminative structured learning, distributional semantics and contextual evidence extracted from the working environment. The final goal is to make the interpretation process of linguistic exchanges depending on physical, cognitive and language-dependent aspects. We present, formalize and discuss an adaptive Spoken Language Understanding chain for robotic commands, that explicitly depends on the operational context during both the learning and processing stages. The resulting framework allows to model heterogeneous information concerning the environment (e. g. , positional information about the objects and their properties) and to inject it in the learning process. Empirical results demonstrate a significant contribution of such additional dimensions, achieving up to a 25% of relative error reduction with respect to a pipeline that only exploits linguistic evidence.

AAMAS Conference 2019 Conference Paper

Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains

• Jim Martin Catacora Ocana
• Francesco Riccio
• Roberto Capobianco
• Daniele Nardi

In multi-robot reinforcement learning the goal is to enable a group of robots to learn coordinated behaviors from direct interaction with the environment. Here, we provide a comparison of two main approaches designed for tackling this challenge; namely, independent learners (IL) and joint-action learners (JAL). We evaluate these methods in a multi-robot cooperative and adversarial soccer scenario, called 2 versus 2 free-kick task, with simulated NAO humanoid robots as players. Our findings show that both approaches can achieve satisfying solutions, with JAL outperforming IL.

AAMAS Conference 2019 Conference Paper

Summary: Distributed Task Assignment and Path Planning with Limited Communication for Robot Teams

• Dario Albani
• Wolfgang Höenig
• Nora Ayanian
• Daniele Nardi
• Vito Trianni

AAMAS Conference 2018 Conference Paper

DOP: Deep Optimistic Planning with Approximate Value Function Evaluation

• Francesco Riccio
• Roberto Capobianco
• Daniele Nardi

Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e. g. multi-agent systems or hyper-redundant robots). To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, that attacks the curse of dimensionality and reduces the computational demand of the planning process while achieving good performance.

AAMAS Conference 2018 Conference Paper

Dynamic UAV Swarm Deployment for Non-Uniform Coverage

• Dario Albani
• Tiziano Manoni
• Daniele Nardi
• Vito Trianni

In many monitoring and mapping applications, high-resolution data are required only in certain areas while others can receive lower attention. To this end, unmanned aerial vehicles (UAVs) can adjust the flight altitude to increase the resolution only where needed, making non-uniform coverage strategies efficient both in time and energy expenditure. In a multi-UAV monitoring context, it is necessary to deploy UAVs to inspect in parallel those areas where a higher resolution is required. To address this problem, we propose a decentralised deployment strategy inspired by the collective behaviour of honeybees. This strategy dynamically assigns UAVs to different areas to be monitored, and suitably re-assigns them to other areas when needed. We introduce an analytical macroscopic model of area monitoring from UAVs, and we propose a parameterisation that leads to an efficient allocation of UAVs to the areas to be monitored. We exploit abstract multi-agent simulations to study the dynamics of the deployment of UAVs to multiple areas, and we present results with simulations of a UAV swarm engaged in a weed monitoring and mapping task.

AAMAS Conference 2018 Conference Paper

Incrementally Learning Semantic Attributes through Dialogue Interaction

• Andrea Vanzo
• Jose L. Part
• Yanchao Yu
• Daniele Nardi
• Oliver Lemon

Enabling a robot to properly interact with users plays a key role in the effective deployment of robotic platforms in domestic environments. Robots must be able to rely on interaction to improve their behaviour and adaptively understand their operational world. Semantic mapping is the task of building a representation of the environment, that can be enhanced through interaction with the user. In this task, a proper and effective acquisition of semantic attributes of targeted entities is essential for the task accomplishment itself. In this paper, we focus on the problem of learning dialogue policies to support semantic attribute acquisition, so that the effort required by humans in providing knowledge to the robot through dialogue is minimized. To this end, we design our Dialogue Manager as a multi-objective Markov Decision Process, solving the optimisation problem through Reinforcement Learning. The Dialogue Manager interfaces with an online incremental visual classifier, based on a Load-Balancing Self-Organizing Incremental Neural Network (LB- SOINN). Experiments in a simulated scenario show the effectiveness of the proposed solution, suggesting that perceptual information can be properly exploited to reduce human tutoring cost. Moreover, a dialogue policy trained on a small amount of data generalises well to larger datasets, and so the proposed online scheme, as well as the real-time nature of the processing, are suited for an extensive deployment in real scenarios. To this end, this paper provides a demonstration of the complete system on a real robot.

ICRA Conference 2018 Conference Paper

Q-CP: Learning Action Values for Cooperative Planning

• Francesco Riccio
• Roberto Capobianco
• Daniele Nardi

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e. g, hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q- CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.

IROS Conference 2017 Conference Paper

Field coverage and weed mapping by UAV swarms

• Dario Albani
• Daniele Nardi
• Vito Trianni

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols.

IJCAI Conference 2016 Conference Paper

A Discriminative Approach to Grounded Spoken Language Understanding in Interactive Robotics

• Emanuele Bastianelli
• Danilo Croce
• Andrea Vanzo
• Roberto Basili
• Daniele Nardi

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language pro- cessing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction.

IROS Conference 2016 Conference Paper

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

• Francesco Riccio
• Emanuele Borzi
• Guglielmo Gemignani
• Daniele Nardi

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.

IROS Conference 2015 Conference Paper

Explicit representation of social norms for social robots

• Fabio Maria Carlucci
• Lorenzo Nardi
• Luca Iocchi
• Daniele Nardi

As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of explicit representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.

IROS Conference 2015 Conference Paper

Multi-robot task acquisition through sparse coordination

• Steven D. Klee
• Guglielmo Gemignani
• Daniele Nardi
• Manuela Veloso

In this paper, we consider several autonomous robots with separate tasks that require coordination, but not a coupling at every decision step. We assume that each robot separately acquires its task, possibly from different providers. We address the problem of multiple robots incrementally acquiring tasks that require their sparse-coordination. To this end, we present an approach to provide tasks to multiple robots, represented as sequences, conditionals, and loops of sensing and actuation primitives. Our approach leverages principles from sparse-coordination to acquire and represent these joint-robot plans compactly. Specifically, each primitive has associated preconditions and effects, and robots can condition on the state of one another. Robots share their state externally using a common domain language. The complete sparse-coordination framework runs on several robots. We report on experiments carried out with a Baxter manipulator and a CoBot mobile service robot.

ECAI Conference 2014 Conference Paper

Effective and Robust Natural Language Understanding for Human-Robot Interaction

• Emanuele Bastianelli
• Giuseppe Castellucci
• Danilo Croce
• Roberto Basili 0001
• Daniele Nardi

Robots are slowly becoming part of everyday life, as they are being marketed for commercial applications (viz. telepresence, cleaning or entertainment). Thus, the ability to interact with non-expert users is becoming a key requirement. Even if user utterances can be efficiently recognized and transcribed by Automatic Speech Recognition systems, several issues arise in translating them into suitable robotic actions. In this paper, we will discuss both approaches providing two existing Natural Language Understanding workflows for Human Robot Interaction. First, we discuss a grammar based approach: it is based on grammars thus recognizing a restricted set of commands. Then, a data driven approach, based on a free-from speech recognizer and a statistical semantic parser, is discussed. The main advantages of both approaches are discussed, also from an engineering perspective, i. e. considering the effort of realizing HRI systems, as well as their reusability and robustness. An empirical evaluation of the proposed approaches is carried out on several datasets, in order to understand performances and identify possible improvements towards the design of NLP components in HRI.

ICRA Conference 2013 Conference Paper

Learning environmental knowledge from task-based human-robot dialog

• Thomas Kollar
• Vittorio Perera
• Daniele Nardi
• Manuela Veloso

This paper presents an approach for learning environmental knowledge from task-based human-robot dialog. Previous approaches to dialog use domain knowledge to constrain the types of language people are likely to use. In contrast, by introducing a joint probabilistic model over speech, the resulting semantic parse and the mapping from each element of the parse to a physical entity in the building (e. g. , grounding), our approach is flexible to the ways that untrained people interact with robots, is robust to speech to text errors and is able to learn referring expressions for physical locations in a map (e. g. , to create a semantic map). Our approach has been evaluated by having untrained people interact with a service robot. Starting with an empty semantic map, our approach is able ask 50% fewer questions than a baseline approach, thereby enabling more effective and intuitive human robot dialog.

AAMAS Conference 2011 Conference Paper

Experimental Evaluation of Teamwork in Many-Robot Systems

• Andrea D'Agostini
• Daniele Calisi
• Alberto Leo
• Francesco Fedi
• Luca Iocchi
• Daniele Nardi

This paper describes a Multi-robot System, its features and a set of experiments that show its performance under different conditions (e. g. , network and sensor unrealiability, number of robots, etc). The system has been fully deployed in a simulated environment; in addition, experiments with a hybrid team of cooperating simulated and real robots will be presented.

IROS Conference 2011 Conference Paper

Multi-robot patrolling with coordinated behaviours in realistic environments

• Luca Iocchi
• Luca Marchetti
• Daniele Nardi

Multi-robot patrolling is a fundamental functionality for multi-robot surveillance and environmental monitoring and has been longly investigated. However, benchmarks for multi-robot patrolling, the realization of realistic simulators and testing on real robotic platforms are still very limited. In this paper we discuss the application of state-of-the-art patrolling strategies in realistic applications, showing that it is important to take into account: perception needs of the robots, uncertainty on action execution, characteristics of the environment, closed-loop coordinated behaviors, and realistic simulation environments.

IROS Conference 2010 Conference Paper

A probabilistic action duration model for plan selection and monitoring

• Vittorio A. Ziparo
• Luca Iocchi
• Matteo Leonetti
• Daniele Nardi

The execution of tasks for a robotic agent embedded in a dynamic environment brings about several challenges, due to unpredictable (or unobservable) events, and to inaccurate perception. Moreover, the agent can perform multiple tasks and each task can be achieved by applying different plans, therefore the decision about which strategy is the most convenient, given the current situation of the world, is important for assessing an intelligent overall behavior of the agent. This paper tackles the problem of on-line execution monitoring in a novel way with respect to previous work, since: (1) it considers uncertainty in the duration of actions with a probabilistic model of action duration; (2) it evaluates the cost of each possible plan at run-time in terms of probability of successful termination within a desired expected time. The approach has been evaluated both in a robotic soccer and a surveillance scenario.

AAMAS Conference 2010 Conference Paper

On-line robot execution monitoring using probabilistic action duration

• Vittorio Ziparo
• Luca Iocchi
• Matteo Leonetti
• Daniele Nardi

The execution of tasks for a mobile robot embedded in a dynamicenvironment brings about several challenges, due to the dynamicchanges of the environment and the inaccurate perception of therobot. This paper tackles the problem of on-line execution monitoring when the agent has different tasks and several plan to accomplish them, as in the BDI framework. Our method considersuncertainty in the duration of actions with a probabilistic modelof action duration, and evaluates the cost of each possible plan atrun-time in terms of probability of successful termination within adesired expected time.

ICAPS Conference 2010 Invited Paper

Robotic Agents for Disaster Response Robotics

• Daniele Nardi

In the first part of the talk, I briefly overview the state of the art in the field of disaster response robotics. In the second part of the talk, I present some of the research we developed at Sapienza Univ. of Rome, also in collaboration with the Italian Firemen Department. Specifically, I describe some results in Distributed Situation Assessment, Action Planning and Monitoring, Context-based Design of intelligent robotic agents, Multi-robot Teams for disaster response robotics, and Performance Evaluation Metrics for intelligent robotic agents. Throughout the discussion, I focus on several open challenges that need to be addressed to provide effective solutions for Disaster Response Robotics.

ICAART Conference 2009 Conference Paper

Agent Approach to Situation Assessment

• Giuseppe Paolo Settembre
• Daniele Nardi
• Roberta Pigliacampo

The Situation Assessment process is evolving from signal-analysis based centralized models to high-level reasoning based net-centric models, according to new paradigms of information fusion proposed by recent research. In this paper we propose a knowledge-based approach to Situation Assessment, and we apply it to maritime surveillance. A symbolic model of the world is given to an agent based framework, that use Description Logics based automatic reasoning to devise on estimate of the situation. The described approach potentially allows distributed Situation Assessment through agent collaboration. The goal is to support the understanding of the situation by relying on automatic interpretation processes, in order to provide the human operators with a synthetic vision, pointing out which are the elements on the scenario that require human intervention. The success of high level reasoning techniques is shown through experiments in a real maritime scenario, in which our approach is compared to the performances of human operators which monitor the situation without any support of an automatic reasoning system.

AAMAS Conference 2008 Conference Paper

A Decentralized Approach to Cooperative Situation Assessment in Multi-Robot Systems

• Giuseppe Settembre
• Alessandro Farinelli
• Paul Scerri
• Katia Sycara
• Daniele Nardi

To act effectively under uncertainty, multi-robot teams need to accurately estimate the state of the environment. Although individual robots, with uncertain sensors, may not be able to accurately determine the current situation, the team as a whole should have the capability to perform situation assessment. However, sharing all information with all other team mates is not scalable nor is centralization of all information possible. This paper presents a decentralized approach to cooperative situation assessment that balances use of communication bandwidth with the need for good situation assessment. When a robot believes locally that a particular plan should be executed, it sends a proposal for that plan, to one of its team mates. The robot receiving the plan proposal, can either agree with the plan and forward it on, or it can provide sensor information to suggest that an alternative plan might have higher expected utility. Once sufficient robots agree with the proposal, the plan is initiated. The algorithm successfully balances the value of cooperative sensing against the cost of sharing large volumes of information. Experiments verify the utility of the approach, showing that the algorithm dramatically out-performs individual decisionmaking and obtains performance similar to a centralized approach.

IROS Conference 2008 Conference Paper

OpenRDK: A modular framework for robotic software development

• Daniele Calisi
• Andrea Censi
• Luca Iocchi
• Daniele Nardi

Intense efforts to define a common structure in robotic applications, both from a conceptual and from an implementation point of view, have been carried out in the last years and several frameworks have been realized for helping in developing robotic applications. However, due to the diversity of these applications, as well as of the research groups involved, a common framework is still far from being accepted. In this paper we focus on modularity and re-usability, as major features for robotic applications. We thus characterize existing frameworks for robot software development through the choices made on concurrent execution of modules and information sharing among them and we present OpenRDK, a modular framework focused on rapid development of distributed robotic systems. OpenRDK has been designed and developed with many years of experience following userspsila advice and has been successfully used for the development of many diverse applications with different kinds of robots. After such an extensive test, OpenRDK is now an open source project.

AAMAS Conference 2008 Conference Paper

PNP: A Formal Model for Representation and Execution of Multi-Robot Plans.

• Vittorio Ziparo
• Luca Iocchi
• Daniele Nardi
• Pier Francesco Palamara
• Hugo Costelha

IJCAI Conference 2007 Conference Paper

• Alessandro Farinelli
• Paul Scerri
• Alberto Ingenito
• Daniele Nardi

A prerequisite to efficient behavior by a multi-robot team is the ability to accurately perceive the environment. In this paper, we present an approach to deal with sensing uncertainty at the coordination level. Specifically, robots attach information regarding features that caused the initiation of a course of action, to any coordination message for that activity. Further information regarding such features, acquired by the team, are then combined and the expected utility of the started action is re-evaluated accordingly. Experiments show that the approach allows to coordinate a large group of robots, addressing sensing uncertainty in a tractable way.

ICRA Conference 2007 Conference Paper

Heterogeneous Feature State Estimation with Rao-Blackwellized Particle Filters

• Gian Diego Tipaldi
• Alessandro Farinelli
• Luca Iocchi
• Daniele Nardi

In this paper we present a novel technique to estimate the state of heterogeneous features from inaccurate sensors. The proposed approach exploits the reliability of the feature extraction process in the sensor model and uses a Rao-Blackwellized particle filter to address the data association problem. Experimental results show that the use of reliability improves performance by allowing the approach to perform better data association among detected features. Moreover, the method has been tested on a real robot during an exploration task in a non-planar environment. This last experiment shows an improvement in correctly detecting and classifying interesting features for navigation purpose.

ICRA Conference 2007 Conference Paper

RFID-Based Exploration for Large Robot Teams

• Vittorio A. Ziparo
• Alexander Kleiner
• Bernhard Nebel
• Daniele Nardi

To coordinate a team of robots for exploration is a challenging problem, particularly in large areas as for example the devastated area after a disaster. This problem can generally be decomposed into task assignment and multi-robot path planning. In this paper, we address both problems jointly. This is possible because we reduce significantly the size of the search space by utilizing RFID tags as coordination points. The exploration approach consists of two parts: a stand-alone distributed local search and a global monitoring process which can be used to restart the local search in more convenient locations. Our results show that the local exploration works for large robot teams, particularly if there are limited computational resources. Experiments with the global approach showed that the number of conflicts can be reduced, and that the global coordination mechanism increases significantly the explored area.

ICRA Conference 2006 Conference Paper

Speeding-up Rao-blackwellized SLAM

• Giorgio Grisetti
• Gian Diego Tipaldi
• Cyrill Stachniss
• Wolfram Burgard
• Daniele Nardi

Recently, Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to re-use an already computed proposal distribution. Both techniques substantially speed up the overall process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published, standard datasets illustrate the advantages of our methods over previous Rao-Blackwellized mapping approaches

ICRA Conference 2005 Conference Paper

Task Assignment with Dynamic Perception and Constrained Tasks in a Multi-Robot System

• Alessandro Farinelli
• Luca Iocchi
• Daniele Nardi
• Vittorio A. Ziparo

In this paper we present an asynchronous distributed mechanism for allocating tasks in a team of robots. Tasks to be allocated are dynamically perceived from the environment and can be tied by execution constraints. Conflicts among team mates arise when an uncontrolled number of robots execute the same task, resulting in waste of effort and spatial conflicts. The critical aspect of task allocation in Multi Robot Systems is related to conflicts generated by limited and noisy perception capabilities of real robots. This requires significant extensions to the task allocation techniques developed for software agents. The proposed approach is able to successfully allocate roles to robots avoiding conflicts among team mates and maintaining low communication overhead. We implemented our method on AIBO robots and performed quantitative analysis in a simulated environment.

NMR Workshop 2004 Conference Paper

Qualitative and probabilistic uncertainty in reasoning about actions with sensing

• Luca Iocchi
• Thomas Lukasiewicz
• Daniele Nardi
• Riccardo Rosati 0001

α We present the description logic PN-ALCKNF for reasoning about actions with sensing under qualitative and probabilistic uncertainty, which is an extension of the description logic α ALCKNF by actions with nondeterministic and probabilisα tic effects. We define a formal semantics of PN-ALCKNF in terms of deterministic, nondeterministic, and probabilistic transitions between epistemic states, which are sets of possible states of the world. We introduce the notions of a conditional plan and its goodness under qualitative and probabilistic uncertainty. We then formulate the problem of conditional planning in this framework, and we present an algorithm for solving it. This algorithm is based on a reduction to reasoning in description logics, and is shown to be sound and complete in the sense that it generates all optimal plans. We also describe an application in a robotic-soccer scenario.

ECAI Conference 2004 Conference Paper

Reasoning about Actions with Sensing under Qualitative and Probabilistic Uncertainty

• Luca Iocchi
• Thomas Lukasiewicz
• Daniele Nardi
• Riccardo Rosati 0001

IROS Conference 2003 Conference Paper

Design and evaluation of multi agent systems for rescue operations

• Alessandro Farinelli
• Giorgio Grisetti
• Luca Iocchi
• Sergio Lo Cascio
• Daniele Nardi

The activities of search and rescue of victims in large-scale disasters are very relevant social problems, and from a scientific viewpoint raise many different technical problems in the fields of artificial intelligence, robotics and multi agent systems. In this paper we describe the development of a multi agent system based on the RoboCup Rescue simulator to allow monitoring and decision support, that are needed in a rescue operation. Two significant accomplishments are reported in this paper: the first is a framework for cognitive agent development that provides for the capabilities of information fusion, planning and coordination; the second one is a methodology for evaluation of multi-agent systems in this scenario that aims at measuring not only the efficiency of a system, but also its robustness when conditions in the environment change.

ICRA Conference 2002 Conference Paper

A Biped Locomotion Strategy for the Quadruped Robot Sony ERS-210

• Fabio Zonfrilli
• Giuseppe Oriolo
• Daniele Nardi

We describe the design and implementation of a biped locomotion strategy for the robot Sony ERS-210 (AIBO). Being designed for quadruped gaits, this robot has several limitations which make biped locomotion a challenging task, such as passive feet, a high barycenter in the erect posture, and relatively weak actuators. We have therefore chosen to fully exploit the double support phase, in which the robot has both feet on the ground, in order to achieve the correct take-off conditions for performing the single support phase. During the latter, the mechanism motion is essentially uncontrolled but can be predicted and planned using a simple equivalent mechanical system. Both simulation and experimental results show the positive outcome of our study.

ICRA Conference 2002 Conference Paper

Global Hough Localization for Mobile Robots in Polygonal Environments

• Giorgio Grisetti
• Luca Iocchi
• Daniele Nardi

Knowing the position of a mobile robot in the environment in which it operates is an important element for effectively accomplishing complex tasks requiring autonomous navigation. Among several existing techniques for robot self-localization, a new approach called Hough localization was proposed for map matching in the Hough domain, that turned out to be reliable and efficient for position tracking in polygonal environments. In this paper we present an extension of Hough localization which is able to deal with the global localization problem, in which the robot does not know its initial position in the environment.

ICRA Conference 2001 Conference Paper

A Probabilistic approach to Hough Localization

• Luca Iocchi
• Domenico Mastrantuono
• Daniele Nardi

Autonomous navigation for mobile robots performing complex tasks over long periods of time requires effective and robust self-localization techniques. We describe a probabilistic approach to self-localization that integrates Kalman filtering with map matching based on the Hough transform. Several systematic experiments for evaluating the approach have been performed both on a simulator and on soccer robots embedded in the RoboCup environment.

ECAI Conference 2000 Conference Paper

Artificial Intelligence in RoboCup

• Daniele Nardi

IROS Conference 2000 Conference Paper

Coordination among heterogeneous robotic soccer players

• Claudio Castelpietra
• Luca Iocchi
• Daniele Nardi
• Maurizio Piaggio
• Alessandro Scalzo
• Antonio Sgorbissa

Coordination among multiple robots has been extensively studied, since a number of practical tasks can be performed in a more effective way by employing a fleet of coordinated robotic bases. In particular, distributed coordination among robotic agents has been considered within the framework offered by the robotic soccer competitions. We describe the methods and the results achieved in coordinating the players of the ART team participating in the RoboCup F-2000 league. The team is formed by several heterogeneous robots having different mechanics, different sensors, different control software, and, in general, different abilities for playing soccer. The coordination framework we have developed has been successfully applied during the 1999 official competitions allowing both for a significant improvement of the overall team performance and for a complete interchangeability of all the robots.

IJCAI Conference 1997 Conference Paper

Autoepistemic Description Logics Francesco

• M. Donini
• Daniele Nardi
• Riccardo Rosati

We present Autoepistemic Description Logics (ADLs), in which the language of Description Logics is augmented with modal operators interpreted according to the nonmonotonic logic MKNF. We provide decision procedures for query answering in two very expressive ADLs. We show their representational features by addressing defaults, integrity constraints, role and concept closure. Hence, ADLs provide a formal characterization of a wide variety of nonmonotonic features commonly available in frame-based systems and needed in the development of practical applications.

I&C Journal 1997 Journal Article

The Complexity of Concept Languages

• Francesco M Donini
• Maurizio Lenzerini
• Daniele Nardi
• Werner Nutt

A basic feature of Terminological Knowledge Representation Systems is to represent knowledge by means of taxonomies, here called terminologies, and to provide a specialized reasoning engine to do inferences on these structures. The taxonomy is built through a representation language called aconcept language(ordescription logic), which is given a well-defined set-theoretic semantics. The efficiency of reasoning has often been advocated as a primary motivation for the use of such systems. The main contributions of the paper are: (1) a complexity analysis of concept satisfiability and subsumption for a wide class of concept languages; (2) algorithms for these inferences that comply with the worst-case complexity of the reasoning task they perform.

LOPSTR Conference 1993 Conference Paper

Deductive Synthesis of Programs for Query Answering

• Daniele Nardi
• Riccardo Rosati 0001

Abstract In this paper we discuss the application of the deductive-tableau method for program synthesis to the generation of programs for computing the answers to the queries posed to a relational database. The input-output specification for the synthesis is directly extracted from the expressions of relational calculus. The synthesis is then divided into two steps: the first one produces programs implementing the operators of relational algebra in an applicative, side-effect free language; the second one translates the queries expressed in relational calculus into equivalent expressions of relational algebra. The syntheses have been machine checked using the Deductive Tableau System 1. The work follows a previous application of the deductive-tableau method to the synthesis of programs for data base transactions and aims at developing a test of practical significance for program synthesis.

AIJ Journal 1992 Journal Article

The complexity of existential quantification in concept languages

• Francesco M. Donini
• Maurizio Lenzerini
• Daniele Nardi
• Bernhard Hollunder
• Werner Nutt
• Alberto Marchetti Spaccamela

Much of the research on concept languages, which also are called terminological languages, has focused on the computational complexity of subsumption. The intractability results can be divided into two groups. First, it has been shown that extending the basic language jy- with constructs containing some form of logical disjunction leads to co-NP-hard subsumption problems. Secondly, adding negation to jy- makes subsumption PSPACE-complete. The main result of this paper is that extending jy- with unrestricted existential quantification makes subsumption NP-complete. This is the first proof of intractability for a concept language containing, whether explicitly or implicitly, no construct expressing disjunction. Unrestricted existential quantification is therefore, alongside disjunction, a source of computational complexity in concept languages.

IJCAI Conference 1991 Conference Paper

Tractable Concept Languages

• Francesco M. Donini
• Maurizio Lenzerini
• Daniele Nardi
• Werner

ECAI Conference 1990 Conference Paper

An Efficient Method for Hybrid Deduction

• Francesco M. Donini
• Maurizio Lenzerini
• Daniele Nardi

ECAI Conference 1988 Conference Paper

Belief Revision as Meta-Reasoning

• Maurizio Lenzerini
• Daniele Nardi