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<!DOCTYPE HTML>
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<html>
<head>
<title>ERF2025 - WS42</title>
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<span class="logo"><img src="images/erf2025_logo.jpg" alt="ERF 2025 logo" /></span>
<h1>Advancing AI-Powered Robotic Cognition, Deliberation and Learning for Real-World Applications</h1>
<p>EUROPEAN ROBOTICS FORUM 2025 - WORKSHOP #42</p>
<p>Thursday 27 Mar 2025 / 14:00 - 15:20 / Silchersaal</p>
</header>
<!-- Nav -->
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<ul>
<li><a href="#program" class="active">Program</a></li>
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<!-- Main -->
<div id="main">
<!-- Introduction -->
<section id="program" class="main special">
<div class="spotlight">
<div class="content">
<header class="major">
<h2>Program</h2>
</header>
<!-- <div> -->
<!-- <div style="display: flex; align-items: center; margin-bottom: 10px;">
<img src="images/francisco.jpeg" alt="Francisco Martín Rico" style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>Francisco Martín Rico</strong>, Rey Juan Carlos University, ES<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> Plan Replanning and Repair at PlanSys2<br>
<i><strong>Abstract:</strong></i> PlanSys2 is the reference planning framework in ROS 2. It combines classical planning with Behavior Trees to execute plans on real robots.
However, replanning in such frameworks is typically limited to canceling the current plan and generating a new one from scratch. In this talk, we will explore alternative approaches for adapting to changes in conditions or goals, focusing on repairing the current plan to align with the new situation.
We will discuss how various strategies, including the use of Large Language Models (LLMs) to assist in this task, can enhance the efficiency of plan execution.
</div> -->
<!-- <div style="display: flex; align-items: center; margin-bottom: 10px;">
<img src="images/richard.gif" alt="Richard J. Duro" style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>Richard J. Duro</strong>, Universidad Coruña, ES<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> The Purpose Framework: A Path to Useful Autonomous Robots<br>
<i><strong>Abstract:</strong></i> This talk will introduce the main issues that are being addressed in the PILLAR-Robots project related to Lifelong Open-ended Learning Autonomy (LOLA).
PILLAR-Robots seeks to create the basis for developing a new generation of robots endowed with a higher level of autonomy, able to determine their own goals and strategies creatively, building on the experience acquired during their lifetime to fulfill the desires of their designers/users in real-life application use cases. <br>
The concept of Purpose, drawn from cognitive sciences, is operationalized to increase the autonomy and domain independence of robots during autonomous learning, leading them to acquire knowledge and skills that are actually relevant for operating in target real applications. <br>
In particular, the talk will describe the purpose framework, which has been developed as an integrated motivational structure that supports algorithms for:
- The acquisition of purpose by the robot;
- Ways to bias the perceptual, motivational, and decision systems of the robots' cognitive architectures toward purposes;
- Strategies for learning representations, skills, and models that allow the execution of purpose-related deliberative and reactive decisions.
These ideas are being applied in three different application fields characterized by different types and levels of variability: Agri-food, Edutainment, and unstructured Industrial/retail.
</div> -->
<!-- </div> -->
<div>
<div style="display: flex; align-items: center; margin-bottom: 10px;">
<img src="images/nestor.jpeg" alt="Néstor García"
style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>Néstor García</strong>, Technology Centre of Catalonia Eurecat, ES<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> Rapid Skill Learning with Hand-Held Grippers in IntelliMan<br>
<i><strong>Abstract:</strong></i> Eurecat presented recent advances to the Universal Manipulation Interface – a low-cost, hand-held gripper that streamlines robot teaching through smarter event detection and enhanced motion tracking, using sensor fusion and EKF. Integrated into the IntelliMan project, the system enables intuitive skill transfer in real-world agricultural and manufacturing settings. Demonstrations showed how robots can continuously learn to adapt, detect execution flaws, and acquire new knowledge for complex manipulation across diverse use cases.
</div>
<p style="text-align: center;">
<a href="slides/rapid_skill_learning.pdf" target="_blank">
<img src="images/rapid_skill_learning_thumb.png"
style="width: 200px; border: 5px solid #555;">
</a>
</p>
<div style="display: flex; align-items: center; margin-bottom: 10px;">
<img src="images/dimitrios.jpeg" alt="Dimitrios Giakoumis"
style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>Dimitrios Giakoumis</strong>, CERTH, GR<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> Advancing the Physical Intelligence and Performance of
Robots Towards Human-Like Object Manipulation<br>
<i><strong>Abstract:</strong></i> This presentation will introduce the main concept of
the EC-funded (HE) MANiBOT project, which aims to empower bi-manual, mobile, service
robots with advanced physical capabilities that allow them to perform a wide variety of
manipulation tasks, with highly diverse objects, in a human-like manner and performance,
in diverse, challenging environments.
The talk will discuss MANiBOT's adaptive multi-level robot cycles approach that enables
it to handle complex tasks, the MANiBOT robotic concept, and the enabling technologies
on advanced environment understanding, efficient manipulation techniques, robot
cognitive functions, and physical intelligence.
</div>
<p style="text-align: center;">
<a href="slides/manibot.pdf" target="_blank">
<img src="images/manibot_thumb.png"
style="width: 200px; border: 5px solid #555;">
</a>
</p>
<div style="display: flex; align-items: center; text-align: justify; margin-bottom: 20px;">
<img src="images/JoaoSilverio.jpg" alt="João Silvério"
style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>João Silvério</strong>, German Aerospace Center, DE<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> Leveraging uncertainty and interactive guidance for robot
skill acquisition<br>
<i><strong>Abstract:</strong></i> When acquiring new manipulation skills, robots must
navigate uncertainty while leveraging interactive guidance to adapt to dynamic,
real-world environments. By employing uncertainty-aware motion primitives to model and
refine skills and constrained reinforcement learning to explore within safe and
practical limits, robots can unify human demonstrations, corrective feedback, and task
constraints. In this talk, I will present recent results on uncertainty-aware,
human-centered frameworks that facilitate adaptive and reliable robot learning in
manipulation tasks.
</div>
<p style="text-align: center;">
<a href="slides/silverio.pdf" target="_blank">
<img src="images/silverio_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</p>
<div style="display: flex; align-items: center; text-align: justify; margin-bottom: 20px;">
<img src="images/triebel.jpg" alt="Rudolph Triebel"
style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>Rudolph Triebel</strong>, German Aerospace Center, DE<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> From the Lab to the Kitchen: Data-efficient Learning and
Sim2Real in the EU-Project euRobin<br>
<i><strong>Abstract:</strong></i> This presentation outlines the EU-Project euRobin's
approach to advancing data-efficient learning and simulation-to-reality (Sim2Real)
transfer for robotics. Focusing on robotic manipulation tasks, it explores how
perception systems can classify, detect, and track objects, estimate their poses, and
identify functional grasps. The project emphasizes overcoming key challenges like
inverse problems, the open-world assumption, and adapting from controlled lab
environments to complex real-world settings such as kitchens. Through simulated
training, virtual environments, and real-world transfer, euRobin aims to develop
cognition-enabled, transferable embodied AI for robust robotic applications.
</div>
<p style="text-align: center;">
<a href="slides/lab_to_the_kitchen.pdf" target="_blank">
<img src="images/lab_to_the_kitchen_thumb.png"
style="width: 200px; border: 5px solid #555;">
</a>
</p>
<div style="display: flex; align-items: center; margin-bottom: 10px;">
<img src="images/christian.jpg" alt="Christian Henkel"
style="width: 150px; height: 150px; border-radius: 50%; margin-right: 15px;">
<div>
<strong>Christian Henkel</strong>, Bosch Research, DE<br>
</div>
</div>
<div style="text-align: justify; margin-bottom: 20px;">
<i><strong>Title:</strong></i> How we tell the robot what to do? Deliberation in
CONVINCE<br>
<i><strong>Abstract:</strong></i> In this workshop talk, we explore the question: How do
we tell a robot what to do? focusing on deliberation within the CONVINCE project.
Deliberation, also known as task-planning or mission-planning, is essential for enabling
robots to operate autonomously and adaptively in dynamic environments.<br>
We will present the tools and approaches developed in CONVINCE, including formal
methods, machine learning, and anomaly detection, and discuss how these support robust
decision-making. Drawing from insights gained through the Deliberation Working Group and
a ROSCon workshop, we will address key open questions:
- What do users need to enable effective robot autonomy?
- How does deliberation differ from traditional industrial robot programming?
- How can we balance low-code solutions with flexible configuration?
- How do we ensure explainability and interpretability, aligning with Industry 5.0
principles?
<br>
This talk aims to engage the audience in a discussion on the unique challenges of modern
deliberation, fostering ideas for bridging the gap between robot programming and
autonomous behavior.
</div>
<p style="text-align: center;">
<a href="slides/deliberation.pdf" target="_blank">
<img src="images/deliberation_thumb.png"
style="width: 200px; border: 5px solid #555;">
</a>
</p>
</div>
</p>
</div>
</div>
</section>
<section id="results" class="main special">
<header class="major">
<h2>Workshop Results</h2>
</header>
<p style="text-align: justify;">
Each speaker was given the chance to address questions to the audience. You may download the results
of these questions below.
</p>
<p style="text-align: center;">
<a href="slides/results.pdf" target="_blank">
<img src="images/results_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</p>
<p style="text-align: justify;">
Additionally, also the slides from our speakers are available:
</p>
<span style="text-align: center;">
<a href="slides/rapid_skill_learning.pdf" target="_blank">
<img src="images/rapid_skill_learning_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</span>
<span style="text-align: center;">
<a href="slides/manibot.pdf" target="_blank">
<img src="images/manibot_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</span>
<span style="text-align: center;">
<a href="slides/silverio.pdf" target="_blank">
<img src="images/silverio_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</span>
<span style="text-align: center;">
<a href="slides/lab_to_the_kitchen.pdf" target="_blank">
<img src="images/lab_to_the_kitchen_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</span>
<span style="text-align: center;">
<a href="slides/deliberation.pdf" target="_blank">
<img src="images/deliberation_thumb.png" style="width: 200px; border: 5px solid #555;">
</a>
</span>
</section>
<!-- First Section -->
<section id="scope" class="main special">
<header class="major">
<h2>Scope and Objectives</h2>
</header>
<p style="text-align: justify;">
This workshop will delve into advanced methods for defining robotic behavior, with a focus on
deliberation, adaptability, and real-world applications. Attendees will gain insights from leading
projects, including Intelliman, CONVINCE, PILLAR-Robots, euROBIN, and MANIBOT, which are at the
forefront of cognitive robotics and AI-powered systems. Key discussions will cover the essentials of
robotic deliberation, examining how autonomous, context-aware decision-making can be achieved.
Attendees will share input on the requirements for effective deliberation, the challenges faced, and
the latest technologies enabling these capabilities.
</p>
<p style="text-align: justify;">
The workshop will also explore data-efficient learning and Sim2Real transfer techniques, showcasing
approaches to skill acquisition and task planning in unstructured environments. Projects will
demonstrate strategies for minimizing data requirements and bridging the gap between simulated and
real-world settings. Additionally, we’ll highlight the role of semantic sensor fusion and control
theory to enhance robustness and situational awareness in complex tasks. Through collaborative
discussions, participants will address both challenges and opportunities in cognitive robotics,
aiming to shape the future of AI-powered robots for dynamic, real-world applications.
</p>
</section>
<!-- Second Section -->
<section id="organizers" class="main special">
<header class="major">
<h2>Organizers</h2>
</header>
<p>
<strong>Christian Henkel</strong>, Bosch Research, DE<br>
<strong>Alessio Caporali</strong>, University of Bologna, IT<br>
<strong>Roberto Meattini</strong>, University of Bologna, IT<br>
<strong>Néstor García</strong>, Eurecat, ES<br>
<strong>Dimitra Triantafyllou</strong>, CERTH, GR<br>
</p>
</section>
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