Excited to share that I’m now leading a research team at @rai_inst! We’re training dexterous policies in simulation to be deployed and refined on custom hardware.

We may be looking for interns in this area soon. I’ll be at #CoRL2026 in Austin — DM me if you’d like to chat!

🚨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 🧵👇

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/grasp2dexterit…
🧵👇 (1/9)

Thrilled to share that our paper DISCO: Distilled Tree Search over Continuous Actions has been selected as a 𝐂𝐨𝐑𝐋 𝟐𝟎𝟐𝟔 𝐁𝐞𝐬𝐭 𝐏𝐚𝐩𝐞𝐫 𝐀𝐰𝐚𝐫𝐝 𝐅𝐢𝐧𝐚𝐥𝐢𝐬𝐭! 🏆 Congrats to @_ksaha 🎉

Can robots learn to plan for the long horizon without expensive search at Monte Carlo Tree Search in simulation and iteratively distills its discoveries into a policy that can execute directly, without search at test time. We demonstrate strong performance on long-horizon object packing and stacking, including real-world transfer!

📍 Catch us in Austin!
🎤 Oral: Nov 10, 11:00–11:30 AM | Hilton Austin (Session O6)
📌 Poster: Nov 10, 5:00–6:30 PM | JW Marriott Austin (Session P2, Poster #176)

🔗 distilled-search.github.io

Life update: I recently graduated from CMU RI, and last year I went through the academic faculty job market in robot learning. I was fortunate to receive a lot of help and support from many people throughout the process.

Compared to industry job applications, there are relatively few resources available online about the faculty application process. So I decided to open-source all of my application materials from last year here:

docs.google.com/document/d/1x-…

I hope these materials can be helpful to others who are considering pursuing an academic career!

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 es 🧵👇 #CORL2026

A man in Australia asked his agent (Claude running on OpenClaw) to book him a spot in a popular gym class. The agent found a software vulnerability that let it book the class weeks further ahead than should have been possible. When the user then asked if it could move him up the covered the API had no authorisation checks on cancelling other people’s reservations, so it cancelled the person in the first spot and moved him up the list.

Some people will call this misalignment, but his agent was perfectly aligned to him - it was only trying to help its user get what he wanted. The most important thing about this story, in my opinion, is that it gives you a window into what is about to start happening on a massive scale once millions of people have an agent trying to get their beloved users the best seats, bookings, appointments or reservations through absolutely any means necessary.

Boo! 👻 GHOST has been accepted to #RSS2026!

Can we learn manipulation skills from human video (without action retargeting)?

Yes! GHOST learns generalizable manipulation skills by training a hierarchical policy with an embodiment-agnostic sub-goal predictor + troller.

🧵(1/7)

🧩 #ICML2026 💥 How can a model discover the 3D objects in a scene—their shape, color, and position—without any labels? Introducing 3D-DLP, a self-supervised object-centric model that decomposes colored 3D scenes (RGB-D and voxels) into a set of 3D latent particles.

Excited to present the first-ever 3D, generalizable, handheld tactile imaging system! 👇

Our system learns to visualize the inner structure of soft objects purely through touch -
"More with LESS: Local Scene Representation for Tactile Imaging," has been accepted to #RSS2026 🇦🇺
e the locality of touch for large-scale tactile SSL? Read on! 🧵

My first last-author paper, with @lakshitadodeja and @rai_inst collaborators!

When deploying Behavior Cloning (BC) policies, we encounter performance gaps due to compounding error, subtle task shifts, etc.

We want to be able to post-train BC policies with a few online interactions to quickly improve performance.

Q2RL is what we hoped for in a robot post-training algorithm:
- ✅it starts with BC policy rollouts (no need for large, diverse datasets), and
- ✅it's sample-efficient enough for real-world online reinforcement learning!

See the thread and linked website for more details. Paper and code are released!

I’m attending #ICLR2026 in Rio this week to present LPWM! Friday April 24 Poster Session 3 10:30, Oral Session 4B 3:15p. Happy to chat about self-sup object-centric learning and world models. I’ll be on the job market soon and looking for exciting opportunities!

#ICLR @iclr_conf

I’m so tired of writing rebuttals to this kind of “lack of novelty” review: “This paper trivially combines A, B, and C, so the algorithmic novelty is limited.”

Technically, most (if not all) robotics papers are convex combinations of existing ideas.

I still deeply appreciate A+B+C papers—especially when they deliver:

- New capabilities: the “trivial combination” unlocks behaviors we simply couldn’t achieve before

- Sensible & organic design: A+B+C is clearly the right composition—not some arbitrary A′+B+C′

- Nontrivial interactions: careful analysis of the dynamics, coupling, or failure modes between A, B, C

- Rehabilitating old ideas: A was dismissed for years, but paired with modern B/C, it suddenly works—and teaches us why

- System-level & "interface" insight: the contribution is not any single piece, but how the pieces talk to each other

- Scaling laws or regimes: identifying when/why A+B+C works (and when it doesn’t)

- Engineering clarity: making something actually work robustly in the real world is not “trivial”

- New problem formulations: sometimes the real novelty is in the reformulation—only under this view does A+B+C make sense.

Maybe worth keeping these in mind when reviewing the next A+B+C paper : )

Excellent analysis by @Majumdar_Ani, recommended read!

🚀 #ICLR2026 Oral 💥
How can we design world models that capture object interactions directly from pixels?
Introducing Latent Particle World Models-the first end-to-end self-supervised, object-centric world model, trained from videos, supporting action/img/lang conditioning.
1/n

I am filling out a fellowship application letter of recommendation, and one question is: "Applicant's achievements reflect his or her ability: * - select -Not at All; Well; Moderately Well; Very Well; Extremely Well".
If I select "Well" then what does that imply?

What are the best slides you have seen to explain transformers to someone who has never seen them before?

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

Check out our new CoRL paper on how to find shapes that cause failures for robot manipulation policies!

Check out our CoRL 2025 paper on learning from past interactions; we show, theoretically and experimentally, the benefit of using a generator-verifier paradigm!