loco-manipulation（高动态物体交互方向）
Tasks
- Apply sim to real training optimization strategies
- Build training and evaluation environments in simulation
- Deploy and debug algorithms on real robots
- Design and optimize loco manipulation control
- Develop whole body control algorithms
- Estimate target state and motion prediction
- Implement motion prior and task conditioned policy learning
- Implement reinforcement learning and imitation learning policies
- Integrate perception decision control interface
- Optimize performance and address control delay and noise issues
- Reproduce evaluate and deploy research algorithms
- Train and evaluate motion imitation and tracking methods
Perks/Benefits
- N/A
Skills/Tech-stack
Body Dynamics | C++ | Contact dynamics | Curriculum learning | Domain Randomization | Imitation Learning | Isaac Lab | Isaac-Gym | Learning algorithms | Motion Control | Motion imitation | Motion matching | Motion tracking | Mujoco | Optimal Control | PPO | PyTorch | Python | Reinforcement Learning | Reinforcement learning algorithms | Rigid body | Rigid body dynamics | Robot Kinematics | Robustness Optimization | SAC | Sim-to-Real | Simulation | System Identification | TD3 | Task Conditioned Policy | Trajectory Optimization | Whole-body control
Education
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