{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162523"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162523","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Robust Dexterous Manipulation Enabled by Learning at Scale inSimulation","abstract":"Robots with robust bimanual dexterity have the potential to transform industries such as manufacturing and healthcare by performing complex tasks at human-level proficiency. While end-to-end learning methods have shown promise in achieving this goal, scaling these approaches remains challenging. Existing paradigms suffer from high costs associated with collecting large-scale, high-quality demonstrations on physical systems and face performance saturation due to reliance on offline data. We propose a task-agnostic pipeline that leverages robotics simulation to overcome these limitations. In particular, we introduce DART, a cost-effective, augmented reality, robot teleoperation platform for scalable data collection. We demonstrate through user study that it enables twice the throughput of existing systems. We also present a learning algorithm that integrates real-world demonstrations with reinforcement learning to surpass performance plateaus. Finally, we design a method that zero-shot transfers policies trained in simulation on real robots using only RGB input. Together, these contributions provide a practical and scalable path toward achieving general-purpose dexterous robot manipulation.","abstract_html":"Robots with robust bimanual dexterity have the potential to transform industries such as manufacturing and healthcare by performing complex tasks at human-level proficiency. While end-to-end learning methods have shown promise in achieving this goal, scaling these approaches remains challenging. Existing paradigms suffer from high costs associated with collecting large-scale, high-quality demonstrations on physical systems and face performance saturation due to reliance on offline data. We propose a task-agnostic pipeline that leverages robotics simulation to overcome these limitations. In particular, we introduce DART, a cost-effective, augmented reality, robot teleoperation platform for scalable data collection. We demonstrate through user study that it enables twice the throughput of existing systems. We also present a learning algorithm that integrates real-world demonstrations with reinforcement learning to surpass performance plateaus. Finally, we design a method that zero-shot transfers policies trained in simulation on real robots using only RGB input. Together, these contributions provide a practical and scalable path toward achieving general-purpose dexterous robot manipulation.","abstract_has_math":false,"creators":["Bhatia, Jagdeep Singh"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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While end-to-end learning methods have shown promise in achieving this goal, scaling these approaches remains challenging. Existing paradigms suffer from high costs associated with collecting large-scale, high-quality demonstrations on physical systems and face performance saturation due to reliance on offline data. We propose a task-agnostic pipeline that leverages robotics simulation to overcome these limitations. In particular, we introduce DART, a cost-effective, augmented reality, robot teleoperation platform for scalable data collection. We demonstrate through user study that it enables twice the throughput of existing systems. We also present a learning algorithm that integrates real-world demonstrations with reinforcement learning to surpass performance plateaus. Finally, we design a method that zero-shot transfers policies trained in simulation on real robots using only RGB input. Together, these contributions provide a practical and scalable path toward achieving general-purpose dexterous robot manipulation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Robust Dexterous Manipulation Enabled by Learning at Scale inSimulation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Agrawal, Pulkit"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Bhatia, Jagdeep Singh"],"dc:date.accessioned":["2025-08-27T14:31:16Z"],"dc:date.available":["2025-08-27T14:31:16Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Robots with robust bimanual dexterity have the potential to transform industries such as manufacturing and healthcare by performing complex tasks at human-level proficiency. While end-to-end learning methods have shown promise in achieving this goal, scaling these approaches remains challenging. Existing paradigms suffer from high costs associated with collecting large-scale, high-quality demonstrations on physical systems and face performance saturation due to reliance on offline data. We propose a task-agnostic pipeline that leverages robotics simulation to overcome these limitations. In particular, we introduce DART, a cost-effective, augmented reality, robot teleoperation platform for scalable data collection. We demonstrate through user study that it enables twice the throughput of existing systems. We also present a learning algorithm that integrates real-world demonstrations with reinforcement learning to surpass performance plateaus. Finally, we design a method that zero-shot transfers policies trained in simulation on real robots using only RGB input. 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