Tech Stack

Description

You will lead the development of learning-based dexterous control algorithms for Apptronik's humanoid robot, Apollo, enabling human-like precision in manufacturing and logistics. Your work will span from simulation to real-world deployment, influencing both software architecture and hardware design.

Requirements

• Deep expertise in multi-fingered hand control, grasp planning, and in-hand manipulation with hardware deployment
• Proficiency in learning-based control (e.g., diffusion-based policies, RL)
• Proficiency in Python and real-time robotic software stacks
• Experience with physics engines (IsaacSim, MuJoCo, or Drake)
• 5+ years experience in robotic manipulation or complex motion control (PhD can substitute 2 years)

Responsibilities

• Serve as technical authority for dexterous manipulation, creating long-term roadmap
• Design and enforce foundational software frameworks for manipulation
• Integrate state-of-the-art learning-based policies and vision-integrated systems
• Lead sim-to-real strategy for high-fidelity simulation and policy transfer
• Drive hardware specifications for sensing, degrees of freedom, and torque profiles