Simulation-Based Reinforcement Learning Policy Optimization for
Tactile Manipulation: A Case Study on the Eyesight Hand
Name
Chang_echang25_sb_meche_thesis_2025.pdf
Description
Thesis PDF
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1.63 MB
Format
Adobe PDF
Checksum (MD5)
ac84b2e5cec70ed5cda35239aa21de30
Author(s)
Chang, Ethan
Advisor(s)
Agrawal, Pulkit
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Robotic manipulation remains a complex and unsolved challenge due to the need for adaptability across diverse objects and tasks. In this work, we explore how to train effective manipulation policies using reinforcement learning in simulation for the Eyesight Hand: a novel, low-cost, tactile-enabled robotic hand. We implement a range of experiments in MuJoCo to evaluate the impact of controller types, observation spaces, reward formulations, and curriculum strategies on policy performance. Our findings highlight the benefits of delta position control, a carefully selected observation space including joint states, control vectors, object pose, and contact forces, and success-driven curriculum learning. Our study establishes baseline strategies for training robust, tactile-based policies on this in-house hardware.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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