Jiasheng Hao

About

Jiasheng Hao is a robotics researcher specializing in social robotics, locomotion control, and reinforcement learning for legged systems. His work bridges the gap between human-robot interaction and dynamic movement, with a particular focus on enabling robots to learn complex skills from human demonstrations. Hao’s most cited paper, “A Survey on Media Interaction in Social Robotics” (2015, 9 citations), provides a foundational overview of how robots communicate through media, shaping the field’s understanding of social engagement. His 2018 study, “Learning Jumping Skills From Human with a Fast Reinforcement Learning Framework” (3 citations), introduces a novel framework that accelerates policy learning for bionic robots, tackling the persistent challenge of dynamic locomotion control. This work demonstrates how human demonstrations can guide robots in mastering agile movements like jumping. Additionally, his 2019 paper on “Gait Phase Optimization of Swing Foot for a Quadruped Robot” (2 citations) advances quadrupedal stability by refining foot trajectory planning. Though his citation counts are modest, Hao’s contributions are notable for their practical focus on integrating learning-based methods with real-world robotic applications, offering valuable insights for students and researchers exploring the intersection of AI, biomechanics, and social robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   A Survey on Media Interaction in Social Robotics

   9 citations · 2015
2. 2
   Learning Jumping Skills From Human with a Fast Reinforcement Learning Framework

   3 citations · 2018
3. 3
   Gait Phase Optimization of Swing Foot for a Quadruped Robot

   2 citations · 2019

Key Collaborators

Contact & Links