Yueci Deng
Papers
1
Total Citations
3
H-Index
1
About
Yueci Deng is a rising researcher in robotics and artificial intelligence, with a focus on one-shot learning for bimanual manipulation. Their most notable contribution, "You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations" (2025), introduces a groundbreaking framework that enables robots to acquire complex dual-arm skills from a single video demonstration—a significant leap toward efficient, human-like robotic learning. This work, already garnering early citations, addresses a critical bottleneck in robotics: reducing the need for extensive training data while maintaining dexterity in tasks like assembly or object handling. Deng’s approach leverages video-based imitation learning and modular policy architectures, pushing the boundaries of generalization in robotic control. Their research holds promise for applications in manufacturing, healthcare, and home assistance, where adaptable, sample-efficient robots are essential. As an emerging voice in the field, Deng’s work exemplifies how minimal supervision can unlock maximal robotic capability, inspiring future advances in autonomous systems.
Research Focus
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Top Papers
- 1