全身运控操作研究员
Tasks
- Analyze failure cases and iterate models
- Apply reinforcement learning and imitation learning
- Build vision language action and multimodal policy
- Conduct loco-manipulation research
- Design learning based control using world models
- Develop walking and manipulation coupling models
- Improve whole body coordination control
- Integrate vision language state and actions into closed loop
- Publish research papers and technical reports
- Run high fidelity simulation experiments
- Validate algorithms on real legged and humanoid robots
Perks/Benefits
- Academic publishing support
- High level research environment
- Research platform support
- Team collaboration with hardware and control experts
Skills/Tech-stack
Contact modeling | Diffusion Models | Dynamic stability | Imitation Learning | Isaac Sim | JAX | Linux | Motion Planning | Mujoco | Multimodal Learning | Offline Reinforcement Learning | PyTorch | Python | Reinforcement Learning | Transformer | Vision-language | Vision-language-action | Whole-body control | World Models
Education
Roles
Research Scientist | Researcher | Robotics Researcher | Scientist
Regions
Countries
States
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