arXiv subjects

Matteo Iovino

Publications and source records attributed to Matteo Iovino.

16 recordsLinked to original sources

arXiv2026

Design and Evaluation of an Assisted Programming Interface for Behavior Trees in Robotics

The possibility to create reactive robot programs faster without the need for extensively trained programmers is becoming increasingly important. So far, it has not been explored how various techniques for creating Behavior Tree (BT) program representations could be combined with complete graphical user interfaces (GUIs) to allow a human user to validate and edit trees suggested by automated methods. In this paper, we introduce BEhavior TRee GUI (BETR-GUI) for creating BTs with the help of an AI assistant that combines methods using large language models, planning, genetic programming, and Bayesian optimization with a drag-and-drop editor. A user study with 60 participants shows that by combining different assistive methods, BETR-GUI enables users to perform better at solving the robot programming tasks. The results also show that humans using the full variant of BETR-GUI perform better than the AI assistant running on its own.

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arXiv2025

Learning Stack-of-Tasks Management for Redundant Robots

This paper presents a novel framework for automatically learning complete Stack-of-Tasks (SoT) controllers for redundant robotic systems, including task priorities, activation logic, and control parameters. Unlike classical SoT pipelines-where task hierarchies are manually defined and tuned-our approach optimizes the full SoT structure directly from a user-specified cost function encoding intuitive preferences such as safety, precision, manipulability, or execution speed. The method combines Genetic Programming with simulation-based evaluation to explore both discrete (priority order, task activation) and continuous (gains, trajectory durations) components of the controller. We validate the framework on a dual-arm mobile manipulator (the ABB mobile-YuMi research platform), demonstrating robust convergence across multiple cost definitions, automatic suppression of irrelevant tasks, and strong resilience to distractors. Learned SoTs exhibit expert-like hierarchical structure and adapt naturally to multi-objective trade-offs. Crucially, all controllers transfer from Gazebo simulation to the real robot, achieving safe and precise motion without additional tuning. Experiments in static and dynamic environments show reliable obstacle avoidance, high tracking accuracy, and predictable behavior in the presence of humans. The proposed method provides an interpretable and scalable alternative to manual SoT design, enabling rapid, user-driven generation of task execution hierarchies for complex robotic systems.

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arXiv2025

Design and Integration of Thermal and Vibrotactile Feedback for Lifelike Touch in Social Robots

Zoomorphic Socially Assistive Robots (SARs) offer an alternative source of social touch for individuals who cannot access animal companionship. However, current SARs provide only limited, passive touch-based interactions and lack the rich haptic cues, such as warmth, heartbeat or purring, that are characteristic of human-animal touch. This limits their ability to evoke emotionally engaging, life-like physical interactions. We present a multimodal tactile prototype, which was used to augment the established PARO robot, integrating thermal and vibrotactile feedback to simulate feeling biophysiological signals. A flexible heating interface delivers body-like warmth, while embedded actuators generate heartbeat-like rhythms and continuous purring sensations. These cues were iteratively designed and calibrated with input from users and haptics experts. We outline the design process and offer reproducible guidelines to support the development of emotionally resonant and biologically plausible touch interactions with SARs.

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arXiv2025

Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models

Recent advancements in Large Language Models (LLMs) and Visual Language Models (VLMs) have significantly impacted robotics, enabling high-level semantic motion planning applications. Reinforcement Learning (RL), a complementary paradigm, enables agents to autonomously optimize complex behaviors through interaction and reward signals. However, designing effective reward functions for RL remains challenging, especially in real-world tasks where sparse rewards are insufficient and dense rewards require elaborate design. In this work, we propose Autonomous Reinforcement learning for Complex Human-Informed Environments (ARCHIE), an unsupervised pipeline leveraging GPT-4, a pre-trained LLM, to generate reward functions directly from natural language task descriptions. The rewards are used to train RL agents in simulated environments, where we formalize the reward generation process to enhance feasibility. Additionally, GPT-4 automates the coding of task success criteria, creating a fully automated, one-shot procedure for translating human-readable text into deployable robot skills. Our approach is validated through extensive simulated experiments on single-arm and bi-manual manipulation tasks using an ABB YuMi collaborative robot, highlighting its practicality and effectiveness. Tasks are demonstrated on the real robot setup.

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arXiv2025

The First WARA Robotics Mobile Manipulation Challenge -- Lessons Learned

The first WARA Robotics Mobile Manipulation Challenge, held in December 2024 at ABB Corporate Research in Västerås, Sweden, addressed the automation of task-intensive and repetitive manual labor in laboratory environments - specifically the transport and cleaning of glassware. Designed in collaboration with AstraZeneca, the challenge invited academic teams to develop autonomous robotic systems capable of navigating human-populated lab spaces and performing complex manipulation tasks, such as loading items into industrial dishwashers. This paper presents an overview of the challenge setup, its industrial motivation, and the four distinct approaches proposed by the participating teams. We summarize lessons learned from this edition and propose improvements in design to enable a more effective second iteration to take place in 2025. The initiative bridges an important gap in effective academia-industry collaboration within the domain of autonomous mobile manipulation systems by promoting the development and deployment of applied robotic solutions in real-world laboratory contexts.

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arXiv2024

Automatic Behavior Tree Expansion with LLMs for Robotic Manipulation

Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks or unpredictable environments, while keeping a transparent policy that is readable and verifiable by humans. We propose the method BEhavior TRee eXPansion with Large Language Models (BETR-XP-LLM) to dynamically and automatically expand and configure Behavior Trees as policies for robot control. The method utilizes an LLM to resolve errors outside the task planner's capabilities, both during planning and execution. We show that the method is able to solve a variety of tasks and failures and permanently update the policy to handle similar problems in the future.

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arXiv2024

Comparison between Behavior Trees and Finite State Machines

Behavior Trees (BTs) were first conceived in the computer games industry as a tool to model agent behavior, but they received interest also in the robotics community as an alternative policy design to Finite State Machines (FSMs). The advantages of BTs over FSMs had been highlighted in many works, but there is no thorough practical comparison of the two designs. Such a comparison is particularly relevant in the robotic industry, where FSMs have been the state-of-the-art policy representation for robot control for many years. In this work we shed light on this matter by comparing how BTs and FSMs behave when controlling a robot in a mobile manipulation task. The comparison is made in terms of reactivity, modularity, readability, and design. We propose metrics for each of these properties, being aware that while some are tangible and objective, others are more subjective and implementation dependent. The practical comparison is performed in a simulation environment with validation on a real robot. We find that although the robot's behavior during task solving is independent on the policy representation, maintaining a BT rather than an FSM becomes easier as the task increases in complexity.

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arXiv2024

Behavior Trees in Industrial Applications: A Case Study in Underground Explosive Charging

In industrial applications Finite State Machines (FSMs) are often used to implement decision making policies for autonomous systems. In recent years, the use of Behavior Trees (BT) as an alternative policy representation has gained considerable attention. The benefits of using BTs over FSMs are modularity and reusability, enabling a system that is easy to extend and modify. However, there exists few published studies on successful implementations of BTs for industrial applications. This paper contributes with the lessons learned from implementing BTs in a complex industrial use case, where a robotic system assembles explosive charges and places them in holes on the rock face. The main result of the paper is that even if it is possible to model the entire system as a BT, combining BTs with FSMs can increase the readability and maintainability of the system. The benefit of such combination is remarked especially in the use case studied in this paper, where the full system cannot run autonomously but human supervision and feedback are needed.

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arXiv2023

A Framework for Learning Behavior Trees in Collaborative Robotic Applications

In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.

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arXiv2023

Behavior Trees for Robust Task Level Control in Robotic Applications

Behavior Trees are a task switching policy representation that can grant reactiveness and fault tolerance. Moreover, because of their structure and modularity, a variety of methods can be used to generate them automatically. In this short paper we introduce Behavior Trees in the context of robotic applications, with overview of autonomous synthesis methods.

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arXiv2022

On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications

In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the standard FSM towards BTs. Many works in the literature have highlighted and proven the benefits of such design compared to standard approaches, especially in terms of modularity, reactivity and human readability. However, these works have often failed in providing a tangible comparison in the implementation of those policies and the programming effort required to modify them. This is a relevant aspect in many robotic applications, where the design choice is dictated both by the robustness of the policy and by the time required to program it. In this work, we compare backward chained BTs with a fault-tolerant design of FSMs by evaluating the cost to modify them. We validate the analysis with a set of experiments in a simulation environment where a mobile manipulator solves an item fetching task.

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arXiv2022

Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration

Fast changing tasks in unpredictable, collaborative environments are typical for medium-small companies, where robotised applications are increasing. Thus, robot programs should be generated in short time with small effort, and the robot able to react dynamically to the environment. To address this we propose a method that combines context awareness and planning to learn Behavior Trees (BTs), a reactive policy representation that is becoming more popular in robotics and has been used successfully in many collaborative scenarios. Context awareness allows to infer from the demonstration the frames in which actions are executed and to capture relevant aspects of the task, while a planner is used to automatically generate the BT from the sequence of actions from the demonstration. The learned BT is shown to solve non-trivial manipulation tasks where learning the context is fundamental to achieve the goal. Moreover, we collected non-expert demonstrations to study the performances of the algorithm in industrial scenarios.

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arXiv2022

Interactive Disambiguation for Behavior Tree Execution

In recent years, robots are used in an increasing variety of tasks, especially by small- and medium- sized enterprises. These tasks are usually fast-changing, they have a collaborative scenario and happen in unpredictable environments with possible ambiguities. It is important to have methods capable of generating robot programs easily, that are made as general as possible by handling uncertainties. We present a system that integrates a method to learn Behavior Trees (BTs) from demonstration for pick and place tasks, with a framework that uses verbal interaction to ask follow-up clarification questions to resolve ambiguities. During the execution of a task, the system asks for user input when there is need to disambiguate an object in the scene, when the targets of the task are objects of a same type that are present in multiple instances. The integrated system is demonstrated on different scenarios of a pick and place task, with increasing level of ambiguities. The code used for this paper is made publicly available.

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arXiv2021

Combining Planning and Learning of Behavior Trees for Robotic Assembly

Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well. An increasingly popular reactive policy architecture in robotics is Behavior Trees but as with other architectures, programming time still drives cost and limits flexibility. There are two main branches of algorithms to generate policies automatically, automated planning and machine learning, both with their own drawbacks. We propose a method for generating Behavior Trees using a Genetic Programming algorithm and combining the two branches by taking the result of an automated planner and inserting it into the population. Experimental results confirm that the proposed method of combining planning and learning performs well on a variety of robotic assembly problems and outperforms both of the base methods used separately. We also show that this type of high level learning of Behavior Trees can be transferred to a real system without further training.

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arXiv2020

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent changes to the task. In this paper, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. Moreover, we propose to use a simple simulator for the learning and demonstrate that the learned BTs can solve the same task in a realistic simulator, reaching convergence without the need for task specific heuristics. The learned solution is tolerant to faults, making our method appealing for real robotic applications.

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arXiv2020

A Survey of Behavior Trees in Robotics and AI

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

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