Megan Minju Lee

Hi! I'm a Master's student in Robotic Systems Development (MRSD) at Carnegie Mellon University, where I focus on the intersection of planning, control, and learning. My professional background includes a Software Engineering internship at Rivian and time as a Software Test Engineer at Boston Dynamics.

Currently, I'm exploring how Reinforcement Learning and Vision-Language Models (VLAs) can push the boundaries of robot manipulation and navigation. My goal is to build intelligent, scalable systems that allow robots to navigate and interact with the messiness of the real world.

Megan Lee

Projects

ASPEN

RIO: Flexible Real-time Robot I/O for Cross-Embodiment Robot Learning

Preprint, Under Review

POLARIS

POLARIS: Hybridized classical planning and reinforcement learning framework

Hybridized classical planning and reinforcement learning framework for long-horizon robot manipulation. An LLM decomposes tasks into subgoals via PDDL predicates, then a policy assignment module dynamically selects between classical motion planning (RRT*) and learned RL policies (PPO) per subgoal, combining the reliability of classical methods with the adaptability of learned behaviors, with automatic fallback upon failure.

ASPEN

ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding

Preprint, Under Review

ASPEN architecture improves cross-subject generalization in brain-computer interfaces by dynamically weighting more consistent spectral features against temporal signals, significantly reducing the need for individual user calibration.

habitat

VLM-Guided Subgoal Planning for Indoor Navigation in Habitat-Lab

Improve robot navigation efficiency and success rates in the Habitat simulator by integrating a "Think" action that allows PPO-based reinforcement learning policies to leverage Vision-Language Models for generating semantic subgoals.

magic

CAPSTONE: Multi Agent Geometric Inspection and Classification

Bimanual manipulation system designed to perform active inspection through coordinated motion and 3D reconstruction.

bumpkin

Bumpkin: A Fist-Bumping Robot Companion

Robot arm that can identify when a human wishes to fist bump, track the closest fist, continuously tracks the target motion with the use of dynamic trajectory execution, and proceeds to bump when the fist is within a threshold distance from the tracked fist.

Multirobot

Nonlinear Trajectory Optimization for Multiple Robot Arms

Centralized multi-robot offline planner set up as a trajectory optimization problem. Given a set of kinematic collision-free paths for each robot, the controller tries to optimize a safe, smooth, and dynamically feasible local trajectory that can be directly executed on all robots online using DIRCOL and ALTRO.