Robotic Systems Lab
@leggedrobotics · Zurich, Switzerland · joined 07 Mar 2016
The Robotic Systems Lab designs machines, creates actuation principles, and builds up control technologies for autonomous operation in challenging environments.
38 268Followers
171Following
704Posts total
130KViews on collected posts
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VLMs don't understand a robot's body. How can a robot learn from its failures without retraining?
PragmaBot: online in-context learning from real-world experience. The robot reflects on failures, stores lessons in memory, and retrieves them for new tasks.
RA-L · #IROS2026 https
4.2K views · 51 likes
· 7 reposts · 1 replies
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0:42
We are releasing EgoHTR, a dataset with both human motions and terrain references, accepted @corl_conf.
📖 Paper: https://t.co/LHEQpqFrXG
🌐 Project Page: https://t.co/x12lTzVTDf
• 55 scene-aligned sequences • 150k+ frames • rough-terrain environments • multi-modal 3D scene https
11.1K views · 42 likes
· 9 reposts · 3 replies
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2:05
@leggedrobotics Thanks for this!
I want to start looking into off policy methods for humanoids and this is very useful 🙏🏼
1.4K views · 4 likes
· 1 reposts · 1 replies
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This project is led by Gianluca Sabatini, supported by Chenhao Li @breadli428 and Marco Hutter @leggedrobotics. We thank Clemens Schwarke's implementation insights.
Check out the paper!
https://t.co/aVPFfzRecM
1.2K views · 8 likes
· 0 reposts · 0 replies
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The implementation extends RSL-RL with same APIs, same training pipeline, same wrappers.
✅Swap the agent config, and you're running RSL-RL-SAC. Multi-GPU, RND, and symmetry augmentation are included out of the box.
https://t.co/muvEjmdFYl
724 views · 7 likes
· 0 reposts · 1 replies
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This project focuses on understanding the performance gap between PPO and SAC in massively parallel robot learning.
While efforts like FlashSAC @hojoon_ai and FastSAC @younggyoseo explore separate development, RSL-RL-SAC is made to stay close to the widely used RSL-RL codebase.
1.6K views · 5 likes
· 0 reposts · 1 replies
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On humanoid tasks, SAC actually outperforms PPO. Entropy-driven exploration pays off in dense reward settings.
Unitree G1: https://t.co/wLgAKplhqb
780 views · 4 likes
· 0 reposts · 1 replies
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By adapting SAC for massively parallel simulation in IsaacLab, we match PPO across quadruped locomotion tasks — with a single set of hyperparameters, NO reward retuning needed.
Unitree Go2: https://t.co/DYbufekzyg
853 views · 3 likes
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To make SAC work well, we crystallize four major factors that matter:
✅Right-sizing the action space
✅Treating timeouts as timeouts, not failures
✅Smoother targets via n-step returns
✅Starting exploration where it should
1.4K views · 9 likes
· 0 reposts · 3 replies
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PPO has been the go-to algorithm for training robots in simulation. SAC is more sample-efficient in theory, but consistently fell short in practice. And while PPO thrives where data is cheap, it hits a hard wall when moving to real-robot learning.
🔥We set out to close that gap.
13.2K views · 23 likes
· 3 reposts · 1 replies
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PPO has long dominated robot locomotion training in simulation. SAC, despite its sample efficiency, couldn't keep up.
We analyze why:
🔗https://t.co/w8cR5lgxjf
🔥Integrated into RSL-RL, our approach requires only minimal changes, making SAC a drop-in alternative out of the box. h
45.6K views · 343 likes
· 42 reposts · 7 replies
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0:19
We’re excited to be receiving one of these platforms for our research. Looking forward to exploring what we can build with it and contributing to the next wave of humanoid robotics.
7.6K views · 50 likes
· 4 reposts · 3 replies
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Advancing dexterous manipulation through scalable visual sim-to-real transfer.
We are excited to share our RSS paper, “ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation.”
🌐 Project page: https://t.co/eFi7QtKee9
1/N 🧵 https://t.co/YbKY5h6wmG
40.4K views · 199 likes
· 20 reposts · 6 replies
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0:30
Dibandingkan akun berukuran sama
2 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. tampil luas, tetapi sedikit penonton yang merespons.
Median tayangan7 649akun ini1 001median untuk 10K–100K
Jangkauan, %19.99%akun ini3.95%median untuk 10K–100K
Interaksi, %0.97%akun ini1.55%median untuk 10K–100K
| Metrik | Akun ini | Median untuk 10K–100K | Rasio |
|---|---|---|---|
| Median tayangan per postingan | 7 649 | 1 001 | 7.64× |
| Jangkauan (tayangan ÷ pengikut) | 19.99% | 3.95% | 5.06× |
| Tingkat interaksi | 0.97% | 1.55% | 0.63× |
Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
40.4K19 May
7.6K1 Jun
45.6K10 Jun
13.2K
1.4K
853
780
1.6K
724
1.2K
1.4K
11.1K15 Sep
4.2K22 Sep
Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.57%19 May
0.76%1 Jun
0.87%10 Jun
0.21%
0.86%
0.47%
0.64%
0.38%
1.10%
0.67%
0.44%
0.50%15 Sep
1.43%22 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.55%.
What the audience does
Likes55.4%748 in total
Reposts6.4%86 in total
Replies2.1%29 in total
Quotes1.2%16 in total
Bookmarks34.9%472 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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