John Emmons — accepted AI-conference papers

2026

MFCL Audio: An Audio Function Calling Evaluation for Large Language Models

Huanzhi Mao, Aditya Ghai, Imra Dawoodani, Tony Ginart, Shishir G. Patil, John Emmons +1

ICML 2026poster

Audio agents are increasingly deployed to execute tools from spoken requests, yet audio tool use poses challenges beyond text-only function calling: perception errors (e.g., homophones, noise, disfluencies) can corrupt entities and arguments, and natural interactions often require clarification that…

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2018

ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung +6

CoRL 2018

Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification. However, research in this area has been limited to modest-sized datasets due to the difficulty of collecting larg

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