Stone Tao

@stonet2000.bsky.social

Simulation/Robot Learning research @physical_int @UCSanDiego, previously @allen_ai @NVIDIA @sudo_robotics http://stoneztao.com

I reverted my decision to hand off ManiSkill to someone else. A ManiSkill 4 is now in progress with lots of improvements (focusing on robot manipulation) If you planned to not use ManiSkill because it might not be maintained once I graduate, I hope this convinces you otherwise!

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would people be interested in a blog series on "simulation infrastructure" where I look at popular robotics sim tools (e.g. mjlab, isaac...) and explain the pros and cons of their design decisions. The series would cover what I believe to be key pillars of good, usable, sim code

I will be in SF from November 10-17 🌉 If you work on something interesting and want to meet up let me know! Or if there’s a fun event i’d love some invites 😃 I’m also looking to visit robotics companies (especially startups!), if you have time lmk if I can visit!

1/1 neurips submission accepted! Will share more details later, covers an often overlooked aspect of on policy RL training when scaling to large scale parallelized environments

Rumors going around are that NeurIPS PCs had to reject ~400 papers because of venue constraints that were in the accept pile. Hard things like this happen but the PCs and conference should explicitly tell all the authors who had this happen to them. Or create something like accept w/o presentation.

My latest post: The American DeepSeek Project Build fully open models in the US in the next two years to enable a flourishing, global scientific AI ecosystem to balance China's surge in open-source and an alternative to building products ontop of leading closed models. buff.ly/kvJQE3I

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Excited to announce that I will be interning at @nvidia research this summer on robotics/embodied AI! I’ll be in seattle for the summer, let me know if you want to meet up and chat! 🦾

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The sim2real demo had some mixed success, hampered primarily by the lighting conditions of the outdoors. At least it worked sometimes! Hindsight says that despite the weather, low-cost nature, only 1 hour of training, anything working is a miracle

Stone Tao@stonet2000.bsky.social · last yr.

I’ll be at #RSS2025 from June 21 to June 23! I’ll be giving a presentation on ManiSkill3 on June 21, 5:30 PM We will also have two live demo sessions, on June 21, 12:30-2:00PM and 6:30-8:00PM. Swing by to see live demos of zero shot RGB sim2real, cool sim demos, and VR teleop!

I’ll be at #RSS2025 from June 21 to June 23! I’ll be giving a presentation on ManiSkill3 on June 21, 5:30 PM We will also have two live demo sessions, on June 21, 12:30-2:00PM and 6:30-8:00PM. Swing by to see live demos of zero shot RGB sim2real, cool sim demos, and VR teleop!

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Rendering nerds! Check out our latest work "Vector-Valued Monte Carlo Integration Using Ratio Control Variates" that has just gotten the best paper award at SIGGRAPH 2025. This paper presents a method that reduces variance of a wide range of rendering and diff. rendering tasks with negligible cost.

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Code/tutorial released for🤗LeRobot zero-shot visual sim2real Reinforcement Learning! Train your SO100 robot in ManiSkill's fast simulator+renderer💨 and deploy a cube picking model zero-shot. Just in time for the LeRobot Hackathon, happy hacking! github.com/StoneT2000/l...

GitHub - StoneT2000/lerobot-sim2real: lerobot sim2real code

lerobot sim2real code. Contribute to StoneT2000/lerobot-sim2real development by creating an account on GitHub.

github.com

sneak peek of what Xander and I will show next week at RSS 2025 for the maniskill demo session: Zero shot visual sim2real (basic) manipulation, one camera and <1 hour of RL in sim with SO100 ~3 seconds to pick random color cubes, could be faster but goal is accessibility and low cost!

Is there any research on replay buffer sizes for off policy (sac) RL algorithms? How come we have just assumed that 1M replay buffer size is reasonable for the majority of tasks we test on (in robotics typically). Why not smaller? Can we make it smaller?

Nice to see the use of ManiSkill3 in this work! Simulation is not just useful for RL training. It provides some good cheap deterministic test beds, perfect for testing imitation learning scaling laws at scale. Years of data in hours

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