Cooperative planning and negotiation in human-robot teams
(English) As robots become increasingly integrated into everyday environments, rigid role paradigms and unilateral control models fall short of enabling meaningful collaboration. Preserving human autonomy while allowing robots to contribute proactively in shared decision-making tasks introduces the...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/448567 |
| Acceso en línea: | https://hdl.handle.net/2117/448567 https://dx.doi.org/10.5821/dissertation-2117-448567 |
| Access Level: | acceso embargado |
| Palabra clave: | human-robot interaction human-robot collaboration human-robot negotiation human-robot plan negotiation human-robot planning multi-agent planning 004 - Informàtica Àrees temàtiques de la UPC::Informàtica |
| Sumario: | (English) As robots become increasingly integrated into everyday environments, rigid role paradigms and unilateral control models fall short of enabling meaningful collaboration. Preserving human autonomy while allowing robots to contribute proactively in shared decision-making tasks introduces the need for alignment and negotiation between agents. Negotiation arises not merely as a design preference but as a requirement when autonomous entities with partial knowledge, differing capabilities, or misaligned goals must act jointly in real-world settings. This thesis investigates the challenge of integrating robots into human teams in unstructured environments, with a particular focus on Human-Robot Collaborative Navigation (HRCN). It seeks to empower them as active decision-making agents who flexibly and critically adapt to human preferences and needs. This technological development is framed as a social necessity: without it, robots would remain confined to controlled environments, or people would lose agency by having to adapt to rigid robot behaviour. The core contributions of the thesis are threefold. First, it introduces the Social Reward Sources (SRS) model, a shared spatial and task representation for Human-Robot Teams (HRT). Second, it presents a multi-agent planning system leveraging the SRS model to generate collaborative plans for heterogeneous teams. Third, it proposes a negotiation framework for Human-Robot Plan Negotiation (HRPN), incorporating a novel plan characterisation model, the cooperativeness space. These and additional secondary contributions are validated through real-world experiments within the collaborative object search benchmark. Altogether, the thesis offers a pathway for deploying robots as collaborative agents capable of negotiation, thereby supporting agency-preserving human-robot interaction in open-world contexts. |
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