{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162710"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162710","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Model-based Planning for Efficient Task Execution","abstract":"Robotic agents navigating 3D environments must continuously decide their next moves by reasoning about both visual observations and high-level language instructions. However, they plan in a high-dimensional latent space, opaque to human collaborators. Hence, it is difficult for humans to understand the agent’s decision-making process. This lack of interpretability hinders effective collaboration between humans and robots. The key question we are trying to answer in this thesis is: Can we build a unified planning framework that fuses visual and language into a single, interpretable representation, so that humans can interpret robots’ decisions? We propose a model-based planning framework built around pretrained vision-language models (VLMs). We show that VLMs can be used to plan in a unified embedding space, where visual and language representations can be decoded back to human-interpretable forms. Empirical evaluation on vision-language navigation benchmarks demonstrates both improved sample efficiency and transparent decision making, enabling human-in-the-loop planning and more effective human-robot collaboration.","abstract_html":"Robotic agents navigating 3D environments must continuously decide their next moves by reasoning about both visual observations and high-level language instructions. However, they plan in a high-dimensional latent space, opaque to human collaborators. Hence, it is difficult for humans to understand the agent’s decision-making process. This lack of interpretability hinders effective collaboration between humans and robots. The key question we are trying to answer in this thesis is: Can we build a unified planning framework that fuses visual and language into a single, interpretable representation, so that humans can interpret robots’ decisions? We propose a model-based planning framework built around pretrained vision-language models (VLMs). We show that VLMs can be used to plan in a unified embedding space, where visual and language representations can be decoded back to human-interpretable forms. Empirical evaluation on vision-language navigation benchmarks demonstrates both improved sample efficiency and transparent decision making, enabling human-in-the-loop planning and more effective human-robot collaboration.","abstract_has_math":false,"creators":["Ding, Wenqi"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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However, they plan in a high-dimensional latent space, opaque to human collaborators. Hence, it is difficult for humans to understand the agent’s decision-making process. This lack of interpretability hinders effective collaboration between humans and robots. The key question we are trying to answer in this thesis is: Can we build a unified planning framework that fuses visual and language into a single, interpretable representation, so that humans can interpret robots’ decisions? We propose a model-based planning framework built around pretrained vision-language models (VLMs). We show that VLMs can be used to plan in a unified embedding space, where visual and language representations can be decoded back to human-interpretable forms. 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However, they plan in a high-dimensional latent space, opaque to human collaborators. Hence, it is difficult for humans to understand the agent’s decision-making process. This lack of interpretability hinders effective collaboration between humans and robots. The key question we are trying to answer in this thesis is: Can we build a unified planning framework that fuses visual and language into a single, interpretable representation, so that humans can interpret robots’ decisions? We propose a model-based planning framework built around pretrained vision-language models (VLMs). We show that VLMs can be used to plan in a unified embedding space, where visual and language representations can be decoded back to human-interpretable forms. Empirical evaluation on vision-language navigation benchmarks demonstrates both improved sample efficiency and transparent decision making, enabling human-in-the-loop planning and more effective human-robot collaboration."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/162710"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Model-based Planning for Efficient Task Execution"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:12Z"}