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Jinghe Wang

2 accepted papers

2026

IGen: Scalable Data Generation for Robot Learning from Open-World Images

CVPR 2026

The rise of generalist robotic policies has created an exponential demand for large-scale training data. However, on-robot data collection is labor-intensive and often limited to specific environments. In contrast, open-world images capture a vast diversity of real-world scenes that naturally align

Cited by 0SourceScholar
2025

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning

ICCV 2025poster

Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g., vision models) to tackle complex tasks based on task descriptions. To push this paradigm toward practical applications…