BS, MS, or PhD in Robotics, Computer Science, or a related field.
7+ years of robotics experience, focusing on control and manipulation of humanoid arms.
Strong understanding of motion planning, control theory, and optimization.
Proven ability to implement advanced manipulation algorithms for complex tasks.
Proficiency in C++, Python, and relevant robotics libraries (e.g., MoveIt!, Drake).
Experience with trajectory optimization libraries.
Familiarity with modern deep learning techniques and computer vision algorithms.
Responsibilities
Design and implement advanced manipulation algorithms for humanoid robots.
Develop the manipulation pipeline for grasping, motion planning, and control.
Implement robust control strategies for high dexterity tasks.
Integrate manipulation systems with the robot's whole-body controller.
Create digital twins for simulation and algorithm testing.
Conduct rigorous testing in simulated and real-world environments.
Collaborate with machine learning teams to enhance manipulation capabilities.
Benefits
Shape technology that advances robotics and human interaction.
Collaborate with a diverse and creative team committed to continuous learning.
Access to advanced prototyping tools and a culture of experimentation.
Flexible work environment and equity opportunities.
Full Job Description
Your Role:
Design and implement advanced, dexterous manipulation algorithms for humanoid robots with high DOF and multi-fingered hands.
Develop and optimize the manipulation pipeline, encompassing perception, grasping, trajectory optimization, motion planning, and control. Tackle complex tasks such as precise grasping, dual-arm manipulation, and in-hand object manipulation.
Implement robust control strategies for precise and dexterous manipulation, including force/motion control and visual/tactile servoing.
Integrate the manipulation pipeline with the humanoid robot's whole-body controller and contribute to loco-manipulation development.
Develop digital-twins of the manipulation scenes for algorithm development. Utilize simulators such as MuJoCo and Isaac Sim.
Conduct rigorous testing in both simulated and real-world environments.
Work closely with the machine learning team to help train and deploy models for advanced manipulation tasks.
Stay informed about the latest research and advancements in manipulation, leveraging both traditional methods and deep learning-based approaches.
We're Looking For:
BS, MS, or PhD in Robotics, Computer Science, or a related field.
7+ years of experience (5+ with advanced degree) in robotics with a strong emphasis on control and manipulation of robotics/humanoid arms.
Strong background in motion planning, control theory, and optimization.
Demonstrated expertise in implementing advanced manipulation algorithms for tasks such as pick-and-place, bin picking, door opening, etc.
Proficiency in C++, Python, and relevant robotics libraries (e.g., MoveIt!, Drake, OpenRAVE).
Experience with trajectory optimization libraries (e.g., IPOPT, SNOPT, qpOASES).
Proficiency with modern deep learning techniques for manipulation.
Experience with computer vision algorithms, sensors, point clouds, segmentation and object detection.
Familiar with ROS, LCM, or other middleware for robotic systems.
Preferred or Bonus Qualifications:
Experience in developing algorithms for contact modeling and force/torque estimation with a focus on tactile sensing and feedback for dexterous tasks.
Experience with machine learning for dexterous manipulation, including learning-based grasp strategies, behavior cloning and reinforcement learning.
Publications in top-tier robotics conferences focusing on dexterous manipulation.
Experience in a humanoid robot startup environment.
