Luai Abuelsamen

Hi! I’m a Master’s student in Robotics at UC Berkeley working on embodied AI systems that bridge foundation models with real-world control. My research spans from multimodal perception for robot manipulation to large-scale autonomous systems simulation.

Currently, I work as a Graduate Student Researcher at PATH, building traffic simulation tools to evaluate infrastructure impacts on autonomous vehicle behavior. I also collaborated with the Autonomy, Robotics, and Controls (ARC) Lab on manipulation research combining learning-based control with multimodal perception.

Previously, I studied Mechanical Engineering at McGill University and interned at Tesla, Beta Technologies, and Vention, where I worked on embedded systems, mechatronic design, and simulation tooling for robotic platforms.

Research Interests:

• Foundation models for robotics and embodied AI
• Multimodal perception and imitation learning
• Model-based and learning-based control
• Sim-to-real transfer and real-time planning
• Multi-agent systems and intelligent infrastructure

Multimodal Perception in Imitation Learning

Research collaboration analyzing how RGB-D, proprioceptive, and language inputs affect sample complexity and optimization landscapes in robot manipulation tasks, with theoretical insights and empirical validation.

📄 Paper · 🔗 Code

Imitation Learning with Residual RL Fine-Tuning

Trained imitation learning policies using LeRobot framework and fine-tuned them in simulation with residual reinforcement learning, combining behavior cloning with policy optimization for improved robotic manipulation performance.

🔗 Code

Industrial Robot Motion Planning with GPUs

Integrated NVIDIA cuRobo into modular automation systems for real-time, collision-free trajectory planning in multi-axis robotic platforms, achieving significant speedups for industrial applications.

🔗 Code · 📄 Paper

Rerouting Impacts of Auxiliary Lanes

Conducted microsimulation research using Aimsun Next to evaluate how auxiliary lane removal affects freeway and arterial network performance in mixed-autonomy scenarios. Work performed at UC Berkeley – California PATH.

📄 Paper (To be Presented at TRB 2026)

Coverage Control for Hybrid Aerial / Ground Robot Teams

Developed a two-layer Voronoi-based approach for multi-agent coverage in heterogeneous robot teams, improving emergency response coordination and resilience to sensor loss.

🔗 Code · 📄 Paper

Rocket Landing Trajectory Optimization

Implemented SOCP-based convex optimization in Python for soft landing guidance, accounting for vehicle dynamics, control limits, and environmental constraints with real-time performance.

🔗 Code · 📄 Report

Bicycle State Estimation with Unscented Kalman Filter

Developed a UKF-based state estimator for a nonlinear bicycle model with uncertain parameters, achieving competitive performance (rank 2/40) through optimized process/measurement noise modeling and parameter estimation.

🔗 Code · 📄 Report

LLM-Based Natural Language Drone Control

Rapid prototyping project integrating vision-language models, SLAM, and zero-shot action chunking for voice-controlled quadrotor navigation in real environments. Built for Eric Schmidt AI hackathon.

🔗 Code · 🎥 Video

PCB Design: Raspberry Pi Motor Controller Hat

Custom PCB enabling high-current servo control and force sensing for autonomous robots, with integrated power regulation and I²C multiplexing for research platform development.

🔗 Project Page

BLE-Controlled Mecanum Robot with FreeRTOS

Developed embedded firmware in C for a mecanum-drive mobile robot featuring dual-mode operation (autonomous/manual), BLE GATT server, ultrasonic obstacle detection, and PWM motor control—all orchestrated with real-time FreeRTOS task scheduling.

🔗 Code