去年归国的徐梦迪，成了清华姚班班主任
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TL;DR - Robotics researcher Mengdi Xu, who returned from Stanford to Tsinghua University in 2025, has become a faculty adviser for the elite Yao Class. Her work argues that general-purpose robots need rapid adaptation, reusable skills, and reliable instruction-following—not scaling alone.
- Xu’s CMU award-winning dissertation explored few-shot task adaptation, transferable robot skills, and using LLM knowledge for physical problem-solving.
- Prompting Decision Transformer adapts policies to unseen tasks from short demonstration trajectories without additional parameter fine-tuning.
- RoboTool analyzes physical constraints, selects tools, plans actions, and generates executable code, enabling robots to devise unconventional solutions.
- Her Tsinghua research targets robots that are scalable, adaptable, and reliable, spanning robot learning, human-robot interaction, and AI safety.
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去年归国的徐梦迪，成了清华姚班班主任
Public signals
N/A
TL;DR - Robotics researcher Mengdi Xu, who returned from Stanford to Tsinghua University in 2025, has become a faculty adviser for the elite Yao Class. Her work argues that general-purpose robots need rapid adaptation, reusable skills, and reliable instruction-following—not scaling alone.
- Xu’s CMU award-winning dissertation explored few-shot task adaptation, transferable robot skills, and using LLM knowledge for physical problem-solving.
- Prompting Decision Transformer adapts policies to unseen tasks from short demonstration trajectories without additional parameter fine-tuning.
- RoboTool analyzes physical constraints, selects tools, plans actions, and generates executable code, enabling robots to devise unconventional solutions.
- Her Tsinghua research targets robots that are scalable, adaptable, and reliable, spanning robot learning, human-robot interaction, and AI safety.