David S. Martinez

WORK HISTORY

EDUCATION

ABOUT DAVID S. MARTINEZ

I am a Robot Learning Engineer focused on enabling generalizable dexterous manipulation across diverse humanoid robot embodiments (arms/hands), tasks and natural human instructions. My work combines visual, tactile, proprioceptive and semantic understanding of the real world with Deep Learning to allow robots to perform dexterous manipulation based on human instructions. Expertise: I have developed deep foundation models for generative robotic grasping trained on massive-scale synthetic datasets from simulation. Then integrated them with LLMs and VLMs for prompt based zero-shot grasping. I have also combined imitation learning with dexterous grasp optimization from 3D vision-language features, to enable language and demonstration driven one-shot grasping. In addition, I have applied deep reinforcement learning to train in-hand manipulation policies. My expertise further includes Deep Learning based object detection, instance segmentation, 3D pose estimation and scene reconstruction; on the robotics side, I specialize in large scale robotic simulators, sim-to-real transfer, AI model deployment, robot agent control, teleoperation and dexterous hand control. My goal is to contribute to a world with safe, friendly and intelligent robots among us.