Amber Li

phd student @CMU_Robotics, previously software engineer @twosigma, B.S. & M.Eng. @MIT @MIT_LISLab

[vincent! @vvhuang_]

We trained a decoder to read the internal activations of an LLM and answer questions about what the model will think about or do next.
We find that this decoder can understand LLM behaviors, even when the model itself is confused! (for instance, if the model has been jailbroken)

[Alexis Hao @hao_alexis]

Introducing FMVP: a method that adapts to natural arm motions during robot-assisted dressing.

Pre-trained on vision in sim, fine-tuned with limited real-world vision+force data, and tested in a 12-user, 264-trial study, FMVP is robust across garments and motions. #CoRL2025

[Divyam Goel @divyamgo10]

How do we discover a robot's failure modes before deploying it in the real world? Standard benchmarks often don't capture the full picture, leaving policies vulnerable to plausible variations in object shape.

Thrilled that our work, "Geometric Red-Teaming for Robotic Manipulation," has been accepted as an oral presentation at #CoRL2025! We introduce a framework to automatically find these geometric blindspots.
https://t.co/mh84bi7Wbp
🧵

[Yishu Li @LisaYishu]

A closed door looks the same whether it pushes or pulls. Two identical-looking boxes might have different center of mass. How should robots act when a single visual observation isn't enough?

Introducing HAVE 🤖, our method that reasons about past interactions online! #CORL2025

[Kallol Saha @_ksaha]

🚨Introducing SPOT: Search over Point Cloud Object Transformations. SPOT is a combined learning-and-planning approach that searches in the space of object transformations.

Website: https://t.co/VwiyDI15FC
Paper: https://t.co/GmdF7hN0FG
Code: https://t.co/YC5sO9OZ0K