Stability driven reinforcement learning for robotic construction
This project explores the use of reinforcement learning for autonomous robotic construction of stable structures without scaffolding. The focus lies on designing reward functions that effec- tively guide a robotic agent to connect two fixed points by placing blocks. Two metrics were de- veloped to e...
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2025 |
| 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:2117/445551 |
| Acesso em linha: | https://hdl.handle.net/2117/445551 |
| Access Level: | acceso abierto |
| Palavra-chave: | Reinforcement learning Autonomous robots Aprenentatge per reforç Robots autònoms Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Resumo: | This project explores the use of reinforcement learning for autonomous robotic construction of stable structures without scaffolding. The focus lies on designing reward functions that effec- tively guide a robotic agent to connect two fixed points by placing blocks. Two metrics were de- veloped to evaluate structural stability: one based on the maximum vertical load each block can bear, and another based on the critical tilting angle before collapse. These metrics were used to shape more informative reward signals compared to traditional binary schemes. Results show that this approach improves learning efficiency and leads to the construction of more robust and reliable designs. |
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