New methods for bridging symbolic-geometric reasoning, addressing uncertainty and action learning in task planning for robotics
(English) The physical world exhibits a wide range of obstacles to the application of robotics in a large number of tasks. Scripted behavior and/or teleoperated programs are still used in many industries (e.g. car assembly) because they are robust and reliable. However, their scope is limited and fa...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/452751 |
| Acesso em linha: | https://hdl.handle.net/2117/452751 https://dx.doi.org/10.5821/dissertation-2117-452751 |
| Access Level: | acceso abierto |
| Palavra-chave: | 004 - Informàtica Àrees temàtiques de la UPC::Informàtica |
| Resumo: | (English) The physical world exhibits a wide range of obstacles to the application of robotics in a large number of tasks. Scripted behavior and/or teleoperated programs are still used in many industries (e.g. car assembly) because they are robust and reliable. However, their scope is limited and falls short in less controlled environments. We explore the possibilities of task planning, or Artificial Intelligence (AI) planning, for solving tasks in a more flexible, non-scripted way. In contrast to motion planning, AI planning focuses on high-level decision-making, rather than on concerns such as computation of trajectories and dynamics. Task planning is a very powerful tool for virtual situated agents (e.g. videogames or web services). One of its main advantages is that it allows deliberative, explainable, and adaptive behavior as long as a reliable model of the environment is available. However, the physical world presents some challenges that make the application of AI planning more difficult. This thesis has the following objectives, each one tied to a different challenge: (O1) integration of AI and motion planning; (O2) handling the unintended effects of actions taken by the robot; (O3) performing tasks even when the robot is not aware of all the relevant information; and (O4) automatic learning of action models to avoid the need for handcrafted ones. Our first contributions revolve mainly around objectives O1 to O3, which involve planning and acting. We propose hierarchical paradigms of planning, exploitation of topological properties of a problem for simplifying Markov Decision Processes, and planning alongside physical simulators to minimize the risk of unintended effects. The second part of our contributions focuses on O4, and consists of different algorithms for learning and recognizing STRIPS action schemata. Published results and findings are provided to support each contribution, alongside examples from manipulation scenarios, such as automatic disassembly of electromechanical devices, and socially assistive interactions. |
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