Robot planning

21 ago - Italia
Generative Bionics

About Us Generative Bionics is a deep-tech company building humanoid robot platforms to deploy human-centered Physical AI. We design intelligent, capable machines that work alongside people in real-world environments — developed in Genova, Italy. Role We are looking for a Robot Planning & Autonomy Engineer to design, implement, and deploy planning and autonomy components across our robotic stack. You will work on decision-making, motion/task planning, behavior orchestration, and the integration of autonomy stacks with perception, control, and simulation. You will collaborate closely with roboticists and AI engineers to bring autonomy to Generative Bionics humanoid robots. Responsibilities Design and implement robot autonomy pipelines, spanning task planning, motion planning, behavior execution and verification. Develop planning algorithms for navigation / manipulation / whole-body behaviors (based on platform needs). Build robust state machines / behavior trees / policy orchestration for real-world execution. Integrate autonomy with perception, control, and localization modules.



Improve performance and reliability via profiling, tuning, and system-level debugging. Contribute to simulation workflows and sim-to-real validation strategies. Requirements Master's degree in Engineering, Robotics, Machine Learning or equivalent technical backgrounds. Solid experience in humanoid robotics autonomy and planning, gained in industry or research with real deployments. Knowledge and practical application of VLM, VLA, World Action Models for reasoning and planning. Strong programming skills in Python and/or C++ with clean, production-oriented engineering practices. Working knowledge of ROS/ROS2 and robotic software Middlewares. Comfort working with real robots: logs, sensors, timing, calibration, edge cases, and debugging. Clear communication and the ability to collaborate across disciplines. Experience with LLM tool calling and API integration. Availability to travel nationally

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