Christopher Agia

@agiachris

PhD in Computer Science candidate @Stanford. My interests span learning for robotic planning, control, vision systems, and their interfacing representations.

Stanford, California, USA

chrisagia.com

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Christopher Agia
@agiachris
Jun 25, 2025
What makes data “good” for robot learning? We argue: it’s the data that drives closed-loop policy success! Introducing CUPID 💘, a method that curates demonstrations not by "quality" or appearance, but by how they influence policy behavior, using influence functions. (1/6)
GIF
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Christopher Agia
@agiachris
Sep 26, 2025
We will be presenting 3 exciting papers at @corl_conf! 🎉 1️⃣ CUPID – Data curation for imitation learning 2️⃣ RoboMonkey – Test-time scaling for VLAs 3️⃣ FORTRESS – Safe reactive planning in OOD scenarios Come find us at the poster sessions - we’d love to chat! Links in thread 🧵
Christopher Agia
@agiachris
Jun 25, 2025
What makes data “good” for robot learning? We argue: it’s the data that drives closed-loop policy success! Introducing CUPID 💘, a method that curates demonstrations not by "quality" or appearance, but by how they influence policy behavior, using influence functions. (1/6)
GIF
4
Christopher Agia
@agiachris
Sep 24, 2025
Join us @corl_conf 2025 this Saturday Sep 27th for the workshop on “Making Sense of Data in Robotics!” We have a line up of exciting talks, posters, and tutorials on all topics robotics data - from composition to intepretability! #CoRL2025
Joey Hejna
@JoeyHejna
Sep 24, 2025
It's almost time for #CoRL 2025! A reminder that we're hosting the Data in Robotics workshop this Saturday Sept 27th. We have a packed schedule and are also attempting to livestream the event for those who can't attend in person.
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Christopher Agia
@agiachris
Aug 11, 2025
Join the discussion on what makes robot learning data "good" at our @corl_conf workshop 🚀! Topics include data composition, curation, and interpretability. Submit by Aug 22!
Joey Hejna
@JoeyHejna
Aug 11, 2025
We're hosting the 1st workshop on Making Sense of Data in Robotics at @corl_conf this year! We'll investigate what makes robot learning data "good" by discussing: 🧩 Data Composition 🧹 Data Curation 💡 Data Interpretability Paper submissions are due 8/22/2025! 🧵(1/3)
Christopher Agia
@agiachris
Aug 11, 2025
CUPID has been accepted to @corl_conf (CoRL) 2025! 🎉 We’ve also released the project code — if you’re interested in influence-based data curation for robot imitation learning, check it out: github.com/agiachris/cupid
Christopher Agia
@agiachris
Jun 25, 2025
What makes data “good” for robot learning? We argue: it’s the data that drives closed-loop policy success! Introducing CUPID 💘, a method that curates demonstrations not by "quality" or appearance, but by how they influence policy behavior, using influence functions. (1/6)
GIF
1