Kaihang Ye

About

Kaihang Ye is a researcher advancing the frontier of human-robot collaboration, with a primary focus on motion prediction and physical human-robot interaction. His most cited work, "Long Short-Term Human Motion Prediction in Human-Robot Co-Carrying" (2023, 4 citations), introduces a novel approach using Long Short-Term Memory (LSTM) networks to anticipate human motion in co-carrying tasks. Ye’s key contribution lies in enabling robots to predict long-term human movement targets, allowing the robot to proactively lead the task rather than passively follow. This work addresses a critical challenge in seamless human-robot teamwork, where anticipating human intent is essential for safe and efficient collaboration. By bridging the gap between short-term reactive control and long-term strategic planning, Ye’s research has implications for assistive robotics, manufacturing, and autonomous systems. His method demonstrates how deep learning can enhance robots’ ability to adapt to human partners in real time. As a rising voice in the field, Ye’s work is laying the groundwork for more intuitive and responsive robotic assistants, with potential to transform industries where humans and robots work side by side.

Research Focus

Key Achievements

Top Papers

1. 1
   Long Short-Term Human Motion Prediction in Human-Robot Co-Carrying

   4 citations · 2023

Key Collaborators

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