全身运控操作研究员
Tasks
- Analyze failure cases to improve algorithms
- Apply imitation learning for control
- Apply offline reinforcement learning
- Apply reinforcement learning for control
- Build whole body coordination control
- Conduct simulation experiments and validation
- Contribute to patents open source and technical reports
- Design walking manipulation coupling modeling
- Develop loco-manipulation models
- Generate low level whole body actions
- Implement vision language action task understanding
- Integrate multimodal inputs into closed loop control
- Publish papers in top conferences and journals
- Test and validate on real legged robots
- Track and evaluate research trends
- Use high fidelity simulation for algorithm testing
Perks/Benefits
- Collaborative research environment
- Conference paper support
- Open source contribution opportunities
- Real robot platform access
- Research funding support
Skills/Tech-stack
Contact modeling | Control Theory | Dynamic stability | Imitation Learning | Isaac Sim | JAX | Language Models | Large Language Models | Linux | Motion Planning | Mujoco | Multimodal Learning | Offline Reinforcement Learning | PyTorch | Python | Reinforcement Learning | Transformer | Vision Language Models | Vision-language | Vision-language-action | Whole-body control
Education
Regions
Countries
States
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