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DETAIL-Interact: Dynamic Engagement and Tailored Interactive Learning for Advanced Robotic Systems

Project description

Advancing robot learning and interaction

Robotics has advanced in recent years, driven by innovations in electronics and digital technologies. As a result, modern robotic systems have demonstrated a range of novel interactive and manipulation skills. However, due to the novelty of these capabilities, limited research has been conducted to explain the underlying processes. Supported by the Marie Skłodowska-Curie Actions programme, the DETAIL-Interact project aims to uncover how robots acquire human-like forceful manipulation and interactive skills. The project will focus on contact-rich interactive manipulation tasks, exploring robust generalisability, online adaptation during interaction, and the representation of interactive skills. It will also develop deep generative models for offline imitation learning, enabling robots to learn from human demonstrations.

Objective

The DETAIL-Interact project, short for Dynamic Engagement and Tailored Interactive Learning for Advanced Robotic Systems, aims to demonstrate how robots master human-like interactive and forceful manipulation skills. Current research on robot manipulation primarily focuses on motion planning for contactless tasks. However, the more challenging domain of contact-rich interactive manipulation tasks has not been thoroughly investigated. Three aspects are crucial: interactive skills representation, robust generalizability, and online adaptation during the interaction. DETAIL-Interact seeks to surpass the state-of-the-art in interactive and forceful manipulation by enabling robots to learn from human demonstrations and exploration strategies. This goal will be achieved using Deep Generative Models (DGMs) for offline imitation learning, which will learn interactive motion priors and the representation of interactive skills. These models will handle high-dimensional demonstration data, including visual inputs, multi-channel force feedback, and robot states among others, essential for mastering forceful interactions. Subsequently, Monte Carlo Tree Search (MCTS) will be innovatively integrated into the receding horizon control framework, utilized for the iterative refinement of online movement through learning and adaptation. This method will optimize movement trajectories and applied forces based on feedback from each interaction and the learned interactive skills. Through this iterative process, the robot will be able to adapt to various interaction environments with differing stiffness levels. The iterative learning process will enhance the robots' performance in precision-oriented tasks, such as selective plant pruning, fruit peeling and cutting, and precise chemical powder grinding. Overall, the DETAIL-Interact project aims to enhance robotic capabilities in interactive and forceful manipulation tasks and is expected to push this topic forward to a higher level.

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Topic(s)

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

THE UNIVERSITY OF LIVERPOOL
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 260 347,92
Address
BROWNLOW HILL 765 FOUNDATION BUILDING
L69 7ZX LIVERPOOL
United Kingdom

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Region
North West (England) Merseyside Liverpool
Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (2)

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