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yisha
82 posts
yisha
@yswhynot
building robots - one at a time
San Diego Katılım Eylül 2014
289 Takip Edilen186 Takipçiler
yisha retweetledi
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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Why is one robot headless 😨
Quanquan Peng@QuanquanPeng03
"Cross-embodiment" is a sign of generalization. We’ve seen huge progress in manipulation and navigation — but what about humanoid whole-body control? Can ONE policy control multiple different humanoids? Meet our #ICRA2026 work 🦅EAGLE: Embodiment-Aware Generalist Specialist Distillation for Unified Humanoid Whole-Body Control. Instead of brute-force URDF / morphology domain randomization, we iteratively distill specialists into one generalist. We also find that embodiment-aware representations matter for policy learning. 🔗 website: eagle-wbc.github.io 📜 arXiv: arxiv.org/abs/2602.02960
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yisha retweetledi
Learning world models from touch instead of pixels. Led by @cwj99770123 and @yswhynot
Changwei Jing@cwj99770123
Can we bridge the Sim-to-Real gap in complex manipulation without explicit system ID? 🤖 Presenting Contact-Aware Neural Dynamics — a diffusion-based framework that grounds simulation with real-world touch. Implicit Alignment: No tedious parameter tuning. Tactile-Driven: Captures non-smooth contact events. Consistent: Stable predictions in contact-rich tasks.
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yisha retweetledi
For years, researchers have believed that simulation could help scale up data, but physics remains the main bottleneck for sim-to-real deployment. Aligning simulation with real-world physics using visual 👁️ and contact 🤝 observations through a network could be a promising step. Check out our recent paper to learn more!
Changwei Jing@cwj99770123
Can we bridge the Sim-to-Real gap in complex manipulation without explicit system ID? 🤖 Presenting Contact-Aware Neural Dynamics — a diffusion-based framework that grounds simulation with real-world touch. Implicit Alignment: No tedious parameter tuning. Tactile-Driven: Captures non-smooth contact events. Consistent: Stable predictions in contact-rich tasks.
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yisha retweetledi
Can we bridge the Sim-to-Real gap in complex manipulation without explicit system ID? 🤖
Presenting Contact-Aware Neural Dynamics — a diffusion-based framework that grounds simulation with real-world touch.
Implicit Alignment: No tedious parameter tuning.
Tactile-Driven: Captures non-smooth contact events.
Consistent: Stable predictions in contact-rich tasks.
English
yisha retweetledi
Pleased to announce our work - Cross Embodied Co-Design for Dexterous Hands - was accepted for ICLR 2026!
Our work fully generates novel robot hands and complementary control policies, zero-shot sim-to-real.
See you in Rio! #ICLR2026
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Struggling with in-hand manipulation? Maybe because human-like hands are not the best hands 😉
We show that with co-design we can discover better hand morphologies for your tasks 🌟
Kehlani Fay@Kehlani_Fay
What makes robot hands dexterous? 🤖🖐️ We generate robot hands + control, sim-to-real, in under 24 hours. Paper: Cross-Embodied Co-Design for Dexterous Hands 🔥 Rapid evaluation w/ cross-embodied policies 🦾 Open-source modular hand platform 💡 Automated full-hand generation
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yisha retweetledi
I am at #NeurIPS! I will be around theAgentCompany poster on Wednesday and giving a talk on towards creating future-proof CUA benchmarks at the SEA workshop on Sunday.
Happy to chat about multi-agent, environments, human-ai collaboration, PhD recruitment and more! DM or shuyanzhxxx@gmail.com 🏖️
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Xueyan is amazing! Catch her at Neurips before it’s too late🥹
Xueyan Zou@xyz2maureen
I will join Tsinghua University, College of AI, as an Assistant Professor in the coming month. I am actively looking for 2026 spring interns and future PhDs (ping me if you are in #NeurIPS). It has been an incredible journey of 10 years since I attended an activity organized by Tsinghua University and decided to change my undergraduate major from Economics to Computer Science, inspired by one of the teammates. During the 10 years, I met with appreciation of many wonderful researchers/professors who led me to continued growth. 🐿️ My research focus will continue to be AI & Robotics, with a specific emphasis on Interactive Embodied Intelligence. You can check my homepage to learn more: maureenzou.github.io/lab.html. I am currently local to San Diego and will be attending #NeurIPS. Please ping me over WeChat or Email if any old or new friends are interested in having a coffee chat! (Really looking forward to meeting as many friends as possible at #NeurIPS) [The photo is one of the places that I will miss a lot in the US]
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yisha retweetledi
How do we make dexterous hands handle both power and precision tasks with ease? 🫳👌🫰
We introduce Power to Precision (💪➡️🎯), our new paper that optimizes both control and fingertip geometry to unlock robust manipulation from power grasp to fine-grained manipulations.
With simplified finger motions and augmented fingertips, the hand can perform diverse motions from pinching a nut🔩 to handling a pan🍳. Check the demos below🎥.
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yisha retweetledi
Excited to share that I’ve recently joined the Chinese University of Hong Kong (CUHK) as an Assistant Professor in Mechanical and Automation Engineering! My research will continue to focus on embodied AI & humanoid robotics — legged locomotion, whole-body and dexterous manipulation, robot foundation models, and more.
Hong Kong offers a unique environment: next to mainland China’s rapidly growing robotics ecosystem, while also being an international and globally connected city. A great place to build impactful robotics research.
🎓Multiple PhD openings (legged, manipulation, aerial, dex hand, and more)
🗓️Deadline: Dec 1, HKT (www4.mae.cuhk.edu.hk/research-postg…)
🌍International applicants are encouraged to also apply for HKPFS (cerg1.ugc.edu.hk/hkpfs/index.ht…)
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yisha retweetledi
How to design soft robot hands for general tasks while incorporating control policies? 💡🦾
Introducing CEM-RM — a hybrid optimization + learned reward model framework that:
- Leverages teleop data to guide design search in sim
- Optimizes efficiently with fewer env interactions
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yisha retweetledi
Learning for hardware design strikes again. We use RL to explore the finger length and thickness, tendon routing, and finger position and orientation for better grasping.
This builds on top of our previous work: yswhynot.github.io/codesign-soft/, which only explores stiffness. I am really looking forward to how far we can push this direction.
Xueqian Bai@baicrystal25
How to design soft robot hands for general tasks while incorporating control policies? 💡🦾 Introducing CEM-RM — a hybrid optimization + learned reward model framework that: - Leverages teleop data to guide design search in sim - Optimizes efficiently with fewer env interactions
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I'm very convinced that learning to design robots is different from supervised learning. What we need is a fair evaluation and an RL-type of design exploration, which is how we can get designs beyond human intuition. Check out our recent work on a small step towards this goal!
Xueqian Bai@baicrystal25
How to design soft robot hands for general tasks while incorporating control policies? 💡🦾 Introducing CEM-RM — a hybrid optimization + learned reward model framework that: - Leverages teleop data to guide design search in sim - Optimizes efficiently with fewer env interactions
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The most fun and pretty project I’ve seen this year 🌟
Xueyan Zou@xyz2maureen
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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yisha retweetledi
Check out our work GsWorld on real2sim2real for training robot policy in photo realistic Gaussian Environment, this is an important step for data curation in the field of robot learning. Please check it out!
Guangqi Jiang@LuccaChiang
Ever want to enjoy all the privileged information in sim while seamlessly transferring to the real world? How can we correct policy mistakes after deployment? 👉Introducing GSWorld, a real2sim2real photo-realistic simulator with interaction physics with fully open-sourced code.
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yisha retweetledi
Ever want to enjoy all the privileged information in sim while seamlessly transferring to the real world? How can we correct policy mistakes after deployment?
👉Introducing GSWorld, a real2sim2real photo-realistic simulator with interaction physics with fully open-sourced code.
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I am feeling incredibly grateful🙏 as I share two big milestones.
At @CarnegieMellon, I've been exploring robotics, AI, and human communication. This journey challenged me to grow, reminded me there is always more to learn, and brought amazing people into my life. A few weeks ago, I defended my PhD thesis. 🎓
This wouldn’t have been possible without my advisor, @ybisk, who believed in me even when I didn’t. I’m also thankful to my committee for their research vision and guidance. To my friends, and family - for celebrating my small wins and for lifting me up through the challenges.
Now, as one chapter closes, another begins! I am beyond excited to join @OpenAI to continue building AI systems that better understand and work with people. Looking forward to staying curious, collaborating, and creating impact in what comes next.🚀
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Latent space magic!!🪄
Zhanyi Sun@s_zhanyi
How to prevent behavior cloning policies from drifting OOD on long horizon manipulation tasks? Check out Latent Policy Barrier (LPB), a plug-and-play test-time optimization method that keeps BC policies in-distribution with no extra demo or fine-tuning: project-latentpolicybarrier.github.io
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