phd student @CMU_Robotics, previously @BasisOrg, @twosigma, B.S. & M.Eng. @MIT @MIT_LISLab
Pittsburgh
Joined August 2012
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Amber Li ✈️ CoRL retweeted
Can learning to grasp teach a robot to use tools? 🤖🖐️
Introducing our #CoRL2026 paper:
“From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation”
We turn grasp data into pretraining for autonomous tool use.
🎥 Project & videos: yingyuan0414.github.io/grasp…
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Amber Li ✈️ CoRL retweeted
🚨Introducing DISCO🪩: Distilled Tree Search over Continuous Actions (Best Paper Finalist🏆 at #CoRL2026)
DISCO combines learned policies with Monte-Carlo Tree Search (MCTS) over continuous actions for long-horizon manipulation tasks.
A thread 🧵👇
Amber Li ✈️ CoRL retweeted
Robots can’t always see the physics they act under: hidden mass, friction, kinematic constraints. A scalar reward only says "failed." The outcome of an action says much more.
Introducing SCOUT: test-time policy adaptation from action-outcome feedback. Sim-trained, adapts in the real world in a few tries 🧵👇 #CORL2026
Amber Li ✈️ CoRL retweeted
Agentic real-to-sim is getting increasingly good at rebuilding scenes in a physics simulator. But looking at a scene does not reveal how glue cures, how water heats, or how a fan's wind pushes things. And what if the simulator cannot model these mechanisms at all?
We present EMPIRIC, an agent that learns a residual world model: a physics engine extended with code for the missing mechanisms.
It solves all 25 runs across five simulated domains, where coding-agent baselines solve 14 to 16, and on a real robot it learns wind and domino masses from two gusts.
basisresearch.github.io/empi…
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Amber Li ✈️ CoRL retweeted
The early deadline has passed, but there's still time to submit to LEAP @corl_conf! We're also excited to announce that Dieter Fox has joined our program 🙂 See you in Austin!
Amber Li ✈️ CoRL retweeted
We're excited to announce the 4th Workshop on Learning Effective Abstractions for Planning (LEAP) at #CoRL2026!
Previous LEAP papers have gone on to win awards at main conferences (SymSkill, Universal Visual Decomposer). Yes, we'll take all the credit!
Workshop link 👇
Amber Li ✈️ CoRL retweeted
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)
Amber Li ✈️ CoRL retweeted
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
Amber Li ✈️ CoRL retweeted
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.
georedteam.github.io
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Amber Li ✈️ CoRL retweeted
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
Amber Li ✈️ CoRL retweeted
🚨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: planning-from-point-clouds.g…
Paper: arxiv.org/abs/2509.04645
Code: github.com/kallol-saha/SPOT