Tao Gao

University of California, Los Angeles, University of Electronic Science and Technology of China

Papers

Total Citations

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About

Tao Gao is a pioneering researcher at the intersection of embodied AI, human-robot interaction, and robotic actuation. His work fundamentally advances how machines understand and execute human-like communication in physical spaces. In his highly cited work "YouRefIt" (32 citations), Gao introduced a groundbreaking visual task that requires AI systems to interpret embodied reference—where a person uses both language and gesture to point out an object in a shared environment. This demands sophisticated multimodal understanding and perspective-taking, pushing the boundaries of how robots can collaborate naturally with humans. Simultaneously, Gao is redefining physical robot capabilities. His recent work on the novel multi-configuration elastic actuator (MCEA) addresses a critical challenge in dynamic energy robot systems: achieving both high power modulation and safe collision. By designing actuators with controllable energy flow, he enables robots to be more efficient, powerful, and safer in real-world interactions. This dual focus—bridging cognitive understanding with physical performance—makes Gao’s work essential for the next generation of collaborative robots that can both “think” and “move” like humans.