Haitong Wang

69 posts

Haitong Wang

Haitong Wang

@haitong__wang

PhD Student @UofT, Robot Learning. Prev. @CarnegieMellon | @Beihang1952.

Toronto, Ontario Katılım Mayıs 2020
301 Takip Edilen45 Takipçiler
Haitong Wang retweetledi
Jiawei Yang
Jiawei Yang@JiaweiYang118·
Two months ago, I vaguely posted a number: 0.9 FID, one-step, pixel space. Now it is 0.75, and can be even lower. Many wonder how. I thought it might end as a small FID prank: simple and deliberate. It started with one question: can FID be optimized directly, and what does it reveal? Introducing FD-loss.
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Aaron Tan
Aaron Tan@aaronistan·
We are hosting the largest showing of Lume in Palo Alto Bring your own laundry Details below
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Aaron Tan
Aaron Tan@aaronistan·
We’re a small team in Palo Alto with a contrarian belief. Home robotics won’t start with everything, it will start with the few chores that actually matter, in a form people already understand. This is our story @bySyncere.
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Aaron Tan
Aaron Tan@aaronistan·
Design is everything at @bySyncere Excited to share our vision for the future of home robotics tomorrow.
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Aaron Tan
Aaron Tan@aaronistan·
We just did a private demo of Lume for Jeff Bezos, Will I Am, Dario Amodei, and more. The progress over the past six months has been incredible. The @bySyncere team is so excited to launch this week.
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Aaron Tan
Aaron Tan@aaronistan·
gave a preview of the new Lume design at a16z sr alumni demo day! more tomorrow @bySyncere
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C. Zhang
C. Zhang@ChongZzZhang·
releasing AME2: Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding arxiv.org/abs/2601.08485 In this work, we discuss how to achieve a combination of generalization and agility in legged locomotion, and propose a general solution.
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Haitong Wang
Haitong Wang@haitong__wang·
@ChongZitaZhang yeah, I agree that with full privileged obs, active perception could emerge. I was talking about when the hidden ground-truth can't be inferred from simplified privileged obs. Critic would still be fine to learn, but for actor, variance would be too high, and not ideal for learn
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C. Zhang
C. Zhang@ChongZzZhang·
@haitong__wang sites.google.com/leggedrobotics… I once made a quadruped do fully blind navigation on rough terrains. I just gave full states to the critic, and proprioception to the actor. The active perception just blatantly emerged from RL
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Haitong Wang
Haitong Wang@haitong__wang·
@ChongZitaZhang Unless some obs engineering is done for the critic obs to include camera direction, FOV, etc.
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Haitong Wang
Haitong Wang@haitong__wang·
@ChongZitaZhang it would also be an issue for critic? for example, if the camera (actor obs) doesn't see the object of interest, but the critic (privileged object states) sees that the object is close to the arm EE, it won't be able to learn the right value for pick and place. Unless some obs
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Haitong Wang
Haitong Wang@haitong__wang·
@ChongZitaZhang Using privileged info might not be ideal? as the optimal behavior under partial obs is usually slightly different from the teacher policy with privileged info
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C. Zhang
C. Zhang@ChongZzZhang·
@haitong__wang You can just train a critic with privileged info, actually easy and stable. You can also freeze the policy to initialize the critic.
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Haitong Wang retweetledi
Tairan He
Tairan He@TairanHe99·
Zero teleoperation. Zero real-world data. ➔ Autonomous humanoid loco-manipulation in reality. Introducing VIRAL: Visual Sim-to-Real at Scale. We achieved 54 autonomous cycles (walk, stand, place, pick, turn) using a simple recipe: 1. RL 2. Simulation 3. GPUs Website: viral-humanoid.github.io Arxiv: arxiv.org/abs/2511.15200 Deep dive with me: 🧵
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Keşfet

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