Patrick Lange

Amazon (United States)

Papers

3

Total Citations

93

H-Index

2

About

Patrick Lange is a leading researcher at the intersection of natural language processing and embodied AI, with a primary focus on enabling robots to interact with humans through intuitive conversation. His major contribution is the creation of the **TEACh** (Task-Driven Embodied Agents That Chat) dataset, a landmark resource comprising over 3,000 human-human interactive dialogues. This work, which has garnered **89 citations**, provides a critical foundation for training agents that can not only follow instructions but also use dialogue to resolve ambiguity and recover from mistakes in real-world environments. Lange’s impact extends to industry-defining challenges; he played a key role in introducing the **Alexa Prize SimBot Challenge**, a competition that pushes the boundaries of multimodal, embodied conversational AI. By bridging the gap between language understanding and physical action, Lange’s research is paving the way for a future where robots are not just tools, but collaborative partners capable of natural, back-and-forth communication in human spaces.

Research Focus

Key Achievements

2
H-Index
3
Papers
93
Total Citations
31
Avg Citations/Paper
🏆 Most Cited Paper
TEACh: Task-Driven Embodied Agents That Chat
89 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 47
🏛 Institutions: Amazon (United States)

Top Papers

  1. 1
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago