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About

Deepali Jain is a robotics researcher whose work sits at the cutting edge of agile locomotion, embodied AI, and dexterous robot control. Her research spans the full spectrum of modern robotics challenges — from teaching quadruped robots to match the fluid athleticism of animals, to enabling dual-arm systems to understand and execute open-ended human instructions through natural language. Jain's most recognized contribution, "Barkour: Benchmarking Animal-level Agility with Quadruped Robots" (2023, 13 citations), established a rigorous framework for evaluating legged robot agility, pushing the field toward biologically inspired movement. Her work on embodied AI with dual-arm systems demonstrates a sophisticated integration of Large Language Models with physical robot control, tackling long-horizon tasks through modular, safety-conscious design. Notably, she contributed to the landmark "Achieving Human Level Competitive Robot Table Tennis" project, representing a milestone in real-time, high-speed learned robot behavior. Her involvement in Google DeepMind's Gemini Robotics initiative signals her growing influence in translating large multimodal AI models into capable physical agents. Across her portfolio, Jain consistently bridges theoretical machine learning advances with demanding real-world robotic applications, making her work essential reading for researchers pursuing capable, generalizable robotic systems.