- Queenstown Central Region (Singapore) Singapore
Lokasi Kerja
Penerangan Kerja
Tanggungjawab
The research project focuses on developing real2sim2real learning and control methodologies for acquiring robust dexterous manipulation skills in complex, content-rich environments. The research engineer is expected to contribute to the following key aspects:
a) Develop real-to-simulation modeling and system identification methods that reconstruct manipulation scenes, object properties, contact dynamics, and robot–environment interactions from multimodal real-world observations, enabling high-fidelity and scalable simulation of dexterous manipulation tasks.
b) Design simulation-based skill learning and optimization methods for contact-rich dexterous manipulation, integrating differentiable simulation, trajectory optimization, reinforcement learning, and/or imitation learning to efficiently acquire transferable manipulation policies under complex physical constraints and diverse task conditions.
c) Develop sim-to-real adaptation and closed-loop skill refinement approaches that leverage multimodal sensory feedback, including vision, tactile, and proprioceptive information, to bridge the simulation-to-reality gap and continuously improve manipulation performance in the real world. The resulting real2sim2real framework will support scalable acquisition of generalizable dexterous manipulation skills and facilitate learning from rich human demonstrations, task semantics, and physical interactions for complex robotic manipulation scenarios.
Peringatan Penting
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