phd student on EmbodiedAI
Joined April 2024
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- Following632
- Followers59
- Likes77
Jin Chen retweeted
PingpongDuo
Humanoid table tennis now has doubles, while I'm still single.
proj: archon.tech/research/Pingpon…
abs: arxiv.org/abs/2610.20579
🔬 Sharing EgoHumanoid-V2, my recent work at Tongyi Lab with our collaborators.
Human-only task demonstrations → learned whole-body coordination → zero-shot deployment on real humanoids.
🌱 A step toward scalable WBI foundation models.
🔗 opendrivelab.com/EgoHumanoid…
Jin Chen retweeted
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Jin Chen retweeted
Full episode dropping soon!
Geeking out with @idomihs on EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration opendrivelab.com/EgoHumanoid…
Co-hosted by @micoolcho @DJiafei
Jin Chen retweeted
During today's #ICM2026 opening ceremonies, the IMU announced the 2026 Fields Medals recipients:
@UChicago's Yu Deng, @stonybrooku's John Pardon, @UofT's Jacob Tsimerman, and Hong Wang of @nyuniversity and @Institut_IHES.
Read more: bit.ly/4psOJbU
ALT headshot of Yu Deng
ALT headshot of John Pardon
ALT headshot of Jacob Tsimerman
ALT headshot of Hong Wang
Jin Chen retweeted
This robot is getting way too good at this.
It can be even faster!
A master makes the move
Just enough to score
From : @Accelerated Evolution
Tiny robots on a grid say more about the future of robotics than most people realize.
This is embodied intelligence in motion.
Multi robot coordination, swarm behavior, path planning, collision avoidance, and real time adaptation.
Small demo today.
Autonomous robot fleets tomorrow.
#Robotics #EmbodiedAI #SwarmRobotics
Jin Chen retweeted
🇮🇳 AI DATA RACE: Workers in Indian factories started to wear cameras on their heads, to film their motions so robots can be trained on these videos.
Big robot companies will train their humanoid robots, on movement data from Indian sweatshops … Wild
1/
Jin Chen retweeted
SimScale is selected as CVPR 2026 Oral 🎉🥳
Code, simulation data, and checkpoints have been fully open-sourced.
📷Home: opendrivelab.com/SimScale/
📷Paper: arxiv.org/abs/2511.23369
📷Github: github.com/OpenDriveLab/SimS…
If you find something useful, please give it a star⭐
1/n 🎉 SimScale: Learning to Drive via Real-World Simulation at Scale
🤖 An innovative real-world simulation pipeline and a real-sim co-training strategy that significantly boost the robustness and generalization of any end-to-end planner.
📈 For the first time, we reveal the scaling effect of simulation data in autonomous driving: with zero extra real-world data, simply scaling up simulation alone can keep improving model performance!
Jin Chen retweeted
RISE (1/N)
Before diving into technical details, let's first see what we've achieved on challenging manipulation.
RISE yields absolute performance increases of:
+45% in Backpack Packing
+35% in Box Closing
+35% in Dynamic Brick Sorting
compared to a strong offline RL method (RECAP, re-implementated on pi_0.5, which is an amazing work by @physical_int)
These improvements highlight the effectiveness of leveraging a proper world model for policy optimization for contact-rich manipulation.
🧐Applying world models to improve real-world policy on challenging manipulation tasks used to be considered out of reach.
😌After sustained effort, we’re now seeing encouraging progress.
🚀Thrilled to introduce RISE: Self-Improving Robot Policy with Compositional World Model
opendrivelab.com/kai0-rl/
arxiv.org/abs/2602.11075
RISE is, to our knowledge, the first work to use a world model as an effective learning environment for challenging real-world manipulation, enabling policy improvement on tasks that demand high dynamics, dexterity, and precision.
Incredible teamwork with @lin_kunyang111 @francislee2020 @YueXiangyu @HaoZhao_AIRSUN @smch_1127
Jin Chen retweeted
【1/5】❓Why do Vision-Language Navigation (VLN) agents need "step-by-step" language instructions? No.❌ We only want to give a intent. 📍
Meet SparseVideoNav: a new paradigm that uses video generation to navigate like a human. Tell the destination. Imagine the path. Reach the goal. 🌄🤖
Paper: arxiv.org/abs/2602.05827
Page：opendrivelab.com/SparseVideo…
Github：github.com/OpenDriveLab/Spar…
Jin Chen retweeted
🦾 【MM-Hand 1.0】Open-Source, Lightweight, High-DoF, Multimodal, Modular Design for Easy Disassembly and Modification
🔗 Details: mmlab.hk/research/MM-Hand
🎁 Beta Program: forms.gle/QhaHGCigY6buuSNfA
📮 Inquiries: [email protected]
#MMHand #DexterousHand #Robotics #OpenSource #EmbodiedAI #TactileSensing
Jin Chen retweeted
Glad to share me and my folks work of the last research cycle, good luck and have fun. Upvote on HF is appreciated!
χ0: huggingface.co/papers/2602.0…
χ0, a resource-efficient robotic manipulation framework addresses distributional shifts through model arithmetic, stage-aware advantage estimation, and train-deploy alignment to achieve long-horizon task reliability.
RISE: huggingface.co/papers/2602.1…
RISE, the first work to use a world model as an effective learning environment for challenging real-world manipulation, enabling policy improvement on tasks that demand high dynamics, dexterity, and precision.
EgoHumanoid: huggingface.co/papers/2602.1…
EgoHumanoid, the first endorsement of human-to-humanoid transfer for whole-body loco-manipulation.
SparseVideoNav: huggingface.co/papers/2602.0…
Vision-language navigation systems traditionally require detailed instructions but can be improved by incorporating video generation models with sparse future planning for faster, more efficient real-world deployment.
Jin Chen retweeted
🧐Applying world models to improve real-world policy on challenging manipulation tasks used to be considered out of reach.
😌After sustained effort, we’re now seeing encouraging progress.
🚀Thrilled to introduce RISE: Self-Improving Robot Policy with Compositional World Model
opendrivelab.com/kai0-rl/
arxiv.org/abs/2602.11075
RISE is, to our knowledge, the first work to use a world model as an effective learning environment for challenging real-world manipulation, enabling policy improvement on tasks that demand high dynamics, dexterity, and precision.
Incredible teamwork with @lin_kunyang111 @francislee2020 @YueXiangyu @HaoZhao_AIRSUN @smch_1127
Jin Chen retweeted
Humanoid robots have been prisoners of the lab. We set them free — with human data.
We present EgoHumanoid: The first endorsement of human-to-humanoid transfer for whole-body loco-manipulation.
🔗 Home: opendrivelab.com/EgoHumanoid
📑 Arxiv: arxiv.org/abs/2602.10106
🧵👇
Jin Chen retweeted
Huge shoutout to the entire team and everyone involved behind the scenes! 🙌
KAI0 is going fully open-source this week. 🚀
📄 Paper: arxiv.org/abs/2602.09021 💻 Code: github.com/OpenDriveLab/kai0
Feedback, comments, and ⭐️s are very welcome!
#Robotics #OpenSource #EmbodiedAI #kai0
🧥 Live-stream robotic teamwork that folds clothes. 6 clothes in 3 minutes straight.
χ₀ = 20hrs data + 8 A100s + 3 key insights:
- Mode Consistency: align your distributions
- Model Arithmetic: merge, don't retrain
- Stage Advantage: pivot wisely
🔗 mmlab.hk/research/kai0