Robust Dexterous Manipulation Enabled by Learning at Scale inSimulation
Name
bhatia-jagdeep-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
6.89 MB
Format
Adobe PDF
Checksum (MD5)
4a3444d747d374664ce1689d8590dae8
Author(s)
Bhatia, Jagdeep Singh
Advisor(s)
Agrawal, Pulkit
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
