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About

Tianli Ding is a robotics and machine learning researcher whose work sits at the dynamic intersection of vision-language models, embodied AI, and real-world robotic control. A central focus of Ding's research is enabling robots to understand and act upon natural language instructions, with contributions to landmark projects that have significantly advanced the field. Ding's most influential contribution is the highly cited RT-2 framework (267 citations), which demonstrated how vision-language models trained on Internet-scale data can be transferred directly into robotic control, unlocking emergent semantic reasoning capabilities in physical agents. Complementing this, the "Interactive Language" project (81 citations) established a real-time framework for natural language-instructable robots, complete with open-sourced datasets and benchmarks. Ding has also advanced long-horizon robotic reasoning through RoboVQA and explored high-speed precision robotics via reinforcement learning applied to table tennis, showcasing versatility across both dexterous physical control and high-level reasoning tasks. More recently, Ding has contributed to Gemini Robotics and RT-Affordance, pushing toward generalizable, multimodal robotic systems designed for the physical world. With over 440 cumulative citations, Ding's body of work represents a meaningful and growing contribution to building robots that can truly understand and interact with the world around them.