Tech Stack

Description

You will support the development of learned manipulation behaviors for humanoid robots, working with Learning from Demonstration systems and contributing to tooling, experiments, and analysis. This role involves hands-on work with teleoperation, data collection, and on-robot evaluation to build force-aware manipulation policies.

Requirements

• Pursuing MS or PhD in Robotics, CS, ML, or related field
• Strong software engineering and Python proficiency
• Background in robotics, machine learning, controls, or autonomous systems
• Familiarity with imitation learning, RL, manipulation, force control, or teleoperation
• Comfortable working hands-on with robotic systems in a lab environment

Responsibilities

• Support demonstration data collection via teleoperation
• Build and improve robot learning testbed (teleop interfaces, cameras, sensing)
• Assist with experiments on contact-rich or force-aware manipulation
• Build tooling for data ingestion, annotation, validation, replay, and analysis
• Evaluate learned policies in simulation and on real hardware