@kevin_zakka What if different mesh number (collision mesh
) in different world？As far as i know, warp still can not support this.
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Kunqi Xu
7 posts
Coming soon to mjlab: heterogeneous worlds, aka every world gets its own object 👀
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Kunqi Xu retweetledi
@JitendraMalikCV If exaggeration and false claims only get rewarded (even just temporarily) and not discredited and penalized, then it is the reputation of science and academia that will eventually be tarnished.
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@Fried_rice So Meta paid billions for Manus… and now Claude’s agent framework just leaked for free. 💀
If the whole value was the agent architecture, isn’t that value now… zero?
Genuinely curious😂 how do you see this?
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Claude code source code has been leaked via a map file in their npm registry!
Code: …a8527898604c1bbb12468b1581d95e.r2.dev/src.zip
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Kunqi Xu retweetledi
🚨 Call for Posters: ICRA 2026 Workshop on Synthetic Data for Robot Learning
Synthetic data is transforming robot learning, from physics simulation to world models & generative methods.
📢 Submit your novel ideas!
🗓 Apr 3–17
🏆 Best Poster Award
🔗 sites.google.com/view/icra-2026…
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Kunqi Xu retweetledi
Our Latent Encoder-Decoder code base is fully open sourced, you can train and visualize the latent space:
Code⚙️: github.com/EmptyBlueBox/D…
ArXiv 📚: arxiv.org/abs/2603.10158
#CVPR2026
Guangqi Jiang@LuccaChiang
Ever want to have a single policy to control diverse robots as well as different dexterous hands, or to observe the emergent behavior under cross embodiment training? Introducing our #CVPR2026 paper XL-VLA, Cross-Hand Latent Representation for Vision-Language-Action Models.
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Kunqi Xu retweetledi
Check out our latest work on machine Theory of Mind: #AutoToM ! We propose an approach that (1) combines the open-endedness of LLMs with robustness of Bayesian models; (2) leverages the uncertainties to refine the model, achieving better performance while maintaining low compute.
Chuanyang Jin@chuanyang_jin
How to achieve human-level open-ended machine Theory of Mind? Introducing #AutoToM: a fully automated and open-ended ToM reasoning method combining the flexibility of LLMs with the robustness of Bayesian inverse planning, achieving SOTA results across five benchmarks. 🧵[1/n]
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Kunqi Xu retweetledi
As an AI researcher, are you interested in tracking trends from CV/NLP/ML to robotics—even Nature/Science. Our paper “Real Deep Research for AI, Robotics & Beyond” automates survey generation and trend/topic discovery across fields
🔥Explore RDR at realdeepresearch.github.io
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