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About

Chenzheng Wang is a robotics researcher whose work focuses on advancing the capabilities of humanoid and dual-arm robots for complex, real-world manipulation and locomotion tasks. A key contribution is the development of a model-based method for bimanual steering wheel turning, enabling a seated dual-arm robot to perform this dexterous manipulation without precise knowledge of the steering wheel’s state—a significant step beyond prior single-arm approaches. Wang has also made notable strides in full-body teleoperation, creating an intuitive and versatile system that allows operators to control a humanoid robot’s high degree-of-freedom body, a critical enabler for tasks in hazardous environments. Further impact is seen in robust vehicle ingress, where Wang integrated a finite state machine with deep reinforcement learning to allow humanoid robots to reliably enter vehicles, bridging the gap between structured planning and adaptive learning. With over 20 citations across these core papers, Wang’s work is shaping the future of mobile manipulation and teleoperation, demonstrating how robots can safely and effectively interact with human-designed environments.