Lin Shao

Assistant Professor in Robotics @NUS | Ph.D. @Stanford | Opinions are my own

[Lin Shao @linshaonju]

With differentiable physics-based simulation&rendering, our robot develops a “model” and learns to select informative viewpoints to perform needle threading (a deformable version of peg insertion).

[Lv Jun @JunLv98]

Excited to share SAM-RL, sensing-aware model-based reinforcement learning via differentiable simulation and rendering. It allows a robot to select viewpoints and accomplish the task of needle-threading.
w/Yunhai, Cheng, @shuangz, @linshaonju, @Cewu_Lu
Web https://t.co/VmP8o75WNj

[Jeannette Bohg @leto__jean]

Remember TidyBot?

We developed an easy-to-assemble, customizable, open-source and low-cost version of our holonomic mobile base.

If you are at #CoRL2024 come see our live demo on the 2nd floor and come to Poster 30 at Session 3.

https://t.co/opWh9pg6sJ

[Jingyun Yang @yjy0625]

Want a robot that learns household tasks by watching you?

EquiBot is a ✨ generalizable and 🚰 data-efficient method for visuomotor policy learning, robust to changes in object shapes, lighting, and scene makeup, even from just 5 mins of human videos.

🧵↓

[Heeger @GChongkai]

3 papers in #CoRL_2024! Come and talk with me about world model, equivariant learning, and dexterous manipulation!

RiEMann: Poster Session 1

Workshops: LEAP, X-Embodiment, MAPoDeL, LFDM.

D(R,O)-Grasp gets Best Robotics Paper on MAPoDeL workshop!

[Davide Scaramuzza @davsca1]

We are excited to share our #CORL2024 paper (oral) on "Learning Quadruped Locomotion Using Differentiable Simulation" done in collaboration with Sangbae Kim @MIT. We present a new way to learn to walk in minutes without parallelization, outperforming PPO in sample efficiency!
PDF: https://t.co/rhcPtdrwEm
Video: https://t.co/7RVUCxvSGx

We present a new framework for learning quadruped locomotion. By leveraging differentiable simulation for policy optimization, our approach achieves fast convergence and stable training, significantly outperforming model-free #ReinforcementLearning methods like PPO in sample efficiency. The key enabler is to combine a high-fidelity, non-differentiable simulator for forward dynamics with a simplified surrogate model for gradient backpropagation. Our framework enables learning quadruped walking in simulation in minutes without parallelization. When augmented with GPU parallelization, our approach allows the quadruped robot to master diverse locomotion skills on challenging terrains in minutes.

This work highlights one of the first successful real-world applications of differentiable simulation for quadruped robots, offering a compelling alternative to traditional RL methods.

Kudos to @realyunlong!

@UZH_Science @UZH_en @UZHspacehub @uzh_ifi @ERC_Research @MITMechE

[Heeger @GChongkai]

Check out our paper: https://t.co/tfKeV6slET

Code: https://t.co/kpPJHUttC6

Awesome cooperation with @ZhengrongX, Shuying Deng, Tianhai Liang, Siqi Yang, @linshaonju , and @HarryXu12.

[Alexander Koch @alexkoch_ai]

Folding clothes with $250 robot arms. I've added another motor to improve mobility and extend the reach. The CAD files and the code are public at: https://t.co/2J1aS6Jred
(video at 2x speed)

[Ken Goldberg @Ken_Goldberg]

"Why don't we have better robots yet?": just posted on the @TEDTalks home page under Newest Talks (3rd row from the top) with links to @PieRobotics and @Forbes article on art by Ben Wolff @creativecellist @Berkeley_AI @UCBerkeley @Cal_Engineer https://t.co/PWgyZ5h7c3

[Gordon Wetzstein @GordonWetzstein]

Current 3D generative models are slow and low quality. We present GRM, a large-scale model that reconstructs 3D Gaussians in 0.1s and generates high-quality 3D assets from text or single images in a few seconds.

https://t.co/0D63aKsFa3

Demo: https://t.co/uKAcpQSsQ1

1/4

[Toru @ToruO_O]

Achieving bimanual dexterity with RL + Sim2Real!

https://t.co/AIckktMuTq

TLDR - We train two robot hands to twist bottle lids using deep RL followed by sim-to-real. A single policy trained with simple simulated bottles can generalize to drastically different real-world objects.

[ROBOTERA @roboterax]

🚀RobotEra & @Tsinghua_Uni launch Humanoid-Gym! An open-source sim2real RL framework for humanoid robots. This codebase is verified on RobotEra's humanoid robot XBot (XiaoXing)!

Project page: https://t.co/7rhc16TItt
Github : https://t.co/hE4yxaJSMy

@JianyuChen_THU @wangyenjen

[Dorsa Sadigh @DorsaSadigh]

@corl_conf website and call for papers are live now!

https://t.co/EqUuiZu2hL

Deadline is on June 6!

Looking forward to seeing everyone in Munich this November!

#CoRL2024

[Robotics: Science and Systems @RoboticsSciSys]

Are a senior PhD student or early career researcher? It is time to check and apply for #RSSPioneers2024, held jointly with #RSS2024 conference!

[Lin Shao @linshaonju]

@contactrika Congrats!!!

[Rika Antonova @contactrika]

After a fruitful search for a faculty position in Machine Learning + Robotics, happy to share that I am joining the University of Cambridge.
Interested in working together? Reach out to connect!
(PhD applications due Dec 5th, MPhil - later in Spring)
https://t.co/RvT9CNjfSJ

[Deepak Pathak @deepakpathak]

Thanks to @ROBOTIS, one can now order all the LEAP Hand parts in a bundle (1-click) from robotis website:

=> LEAP parts: https://t.co/9i1cl2Y8xF
=>LEAP LITE parts: https://t.co/tje0DpOtEy

=> LEAP Hand design, code, API, sim2real: https://t.co/hWRJl6Wa0H

https://t.co/4uC002UyWr

[Javier Civera @jcivera]

#ICRA got upgraded to rank A* (maximum rank) in the CORE ranking, well deserved! 👏👏👏 @ieee_ras_icra

[Evan Kirstel #B2B #TechFluencer @EvanKirstel]

🤖 Introducing the future of cleanliness: an artificial intelligence-assisted cleaning robot! Wave goodbye to tedious chores and let cutting-edge AI technology handle the mess #FutureOfWork #AI

[Mike Shou @MikeShou1]

Happy to share our #ICCV2023 work by @weijiawu7 that turns stable diffusion into surprisedly good segmentation model for open-vocab words, like Eiffel Tower or Ultraman

Paper: https://t.co/4xr8TZhp70
Code: https://t.co/z4PTh5pJZn

[Stanford AI Lab @StanfordAILab]

Congratulations to @GuibasLeonidas and colleagues for winning the SIGGRAPH 2023 Test of Time award for their paper “Functional Maps: A Flexible Representation of Maps Between Shapes”!

https://t.co/nw621vs3L3
https://t.co/fuciCoIRqJ

[Nick Sharp @nmwsharp]

Many physical systems are high-dimensional, but we only really care about some low-dimensional subspace.

Our latest work shows how to fit these subspaces as small neural maps automatically, *without* any data as input, just the energy function.

Read on to learn how! (1/N) 🧵