Papers
8
Total Citations
65
H-Index
6
About
Shihao Wang is a robotics researcher whose work lies at the intersection of humanoid robot safety, multi-agent systems, and intelligent perception. His most impactful contributions focus on preventing humanoid robots from falling by enabling them to actively use their environment—specifically, making hand contact with walls or rails during a loss of balance. In his highly cited 2017 paper (15 citations), he introduced a real-time optimal control strategy using a simplified three-link model to compute the best hand contact point, a breakthrough that was later expanded into a unified multi-contact fall mitigation framework (2018, 11 citations) combining inertial shaping, protective stepping, and hand contact via a contact transition tree optimization. Beyond humanoids, Wang has advanced cooperative control for uncertain multi-agent systems (2023, 9 citations), addressing event-triggered output regulation, and has explored path planning for inspection robots using improved ant colony algorithms (2024, 11 citations). His work also extends to 3D object detection in indoor environments and accurate pose measurement for manufacturing. With over 65 total citations and a growing portfolio spanning optimal control, adaptive switching control, and deep learning, Wang is establishing himself as a versatile researcher committed to making robots safer, more autonomous, and more perceptive in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm11 citations · 2024
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- 7Deep Learning based 3D Object Detection in Indoor Environments: A Review2 citations · 2022
- 8Switching Adaptive Control with Applications on Robot Manipulators1 citations · 2022