Yan Ding

Researcher in Shanghai AI Lab

[Yan Ding @yding25]

🌟 Update 🌟
We’ve launched the hardware system, and the accompanying software will be released soon. We welcome fellow researchers to join us in improving the design and advancing this tool together!

[Yan Ding @yding25]

🌟【New Launch | Fast-UMI】🌟

Struggling with costly and complicated robot data collection? Fast-UMI makes it simple—plug-and-play with no SLAM or calibration needed for end-effector poses.

🔗 Learn more: https://t.co/9QXVkeWLrL

[Yan Ding @yding25]

✨ Fast-UMI Highlights ✨
🔹 No need for complex SLAM deployment and calibration—directly obtain end-effector poses
🔹 Plug-and-play design, compatible with various robotic grippers. No need for WSG-50!

[LIDAR@GT @GT_LIDAR]

If you're interested in learning SOTA of optimization-based task and motion planning, please give it a read of our recent survey paper, ranging from classical to learning methods. @ZhaoZhigen @ShuoCheng94 @yding25 @ZiyiZhou2 @ShiqiZhang7 @danfei_xu
https://t.co/yGwntYRNnO

[Yan Ding @yding25]

@chris_j_paxton Is Docker a good option?

[Yan Ding @yding25]

@chris_j_paxton Congrats and good luck. Hope to see your new fantastic work!

[Fangchen Liu @fangchenliu_]

Can we leverage VLMs for robot manipulation in the open world? Checkout our new work MOKA, a simple and effective visual prompting method!

[Yan Ding @yding25]

@cranialxix @BingCompSci @ShiqiZhang7 Thanks, Bo!😀

[Yan Ding @yding25]

@XiaohanZhang220 @BingCompSci @ShiqiZhang7 Thanks😀!

[Yan Ding @yding25]

@chris_j_paxton @BingCompSci @ShiqiZhang7 Thanks😀!

[Yan Ding @yding25]

@ShiqiZhang7 😄thx Shiqi!

[Yan Ding @yding25]

@hausman_k Thanks for broadcasting our team’s work!

[Karol Hausman @hausman_k]

In COWP https://t.co/nYuLjATA7Y the authors introduce a framework that integrates action knowledge and LLMs to enhance open-world task planning. This enables them to handle novel situations by dynamically augmenting action knowledge.

[Karol Hausman @hausman_k]

It took a little longer but our AURO special issue on LLMs in Robotics is finally here: https://t.co/ZsxcX3E5nf

Edited together with @brian_ichter @xf1280 @SongShuran with help from the main AURO editor @gauravsukhatme

Let's go over the papers in the special issue 👇

[Karol Hausman @hausman_k]

The reason why physical AI is harder than generative AI is because of their respective customers.

Humans can easily forgive any errors, physics is unforgivable.

[Yan Ding @yding25]

We submit this work to LangRob Workshop at CoRL 2023! See you in the poster session!

[Shiqi Zhang @ShiqiZhang7]

Our COWP work is part of this special issue. COWP is for robot planning and situation handling in open worlds (new requests, new objects, new situations, etc) by enabling the marriage between LLM-based reasoning and knowledge-based classical planning. https://t.co/kMScbEIwjL

[Karol Hausman @hausman_k]

Nice work from @leto__jean 's lab led by Jingyun Yang @yjy0625 and Congyue Deng showing how to build SIM(3)-equivariant representations for manipulation.

A few notes: 👇

[Yan Ding @yding25]

@zhenjun_zhao What is GT in Figure 3? It looks good.

[Shiqi Zhang @ShiqiZhang7]

Thanks @CACMmag for highlighting our seeing-eye robot work! It will be presented at #CoRL2023 next week.

[Chris Paxton @chris_j_paxton]

See LLM-GROP by Yan Ding and @XiaohanZhang220 at IROS

Extract semantic knowledge from LLMs like GPT to perform multi step object rearrangement on real robots, like setting the table