Chenhao Li @breadli428
Robotics @GoogleDeepMind | Embodied intelligence and robot learning | Doctoral fellow @ETH_AI_Center, @leggedrobotics | Prev. @MIT, @ETH_en, @MPI_IS. breadli428.github.io Zurich, Switzerland Joined December 2014-
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Paper 1
I decided to review and explicitly post about the limitations of all my previous papers because I believe this is the fundamental driving force behind research, especially in this era of PRs and bubbles.
🎯 Limitations: The reward signal obtained from video models is still rather high-level and not always reliable. Regularization rewards still play a key role especially when targeting for really deployable policies.
🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) @CVPR, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗nil.is.tue.mpg.de
📌 Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking. Humanoid robots face a major challenge in real-world environments: They must go beyond simple walking and handle complex interactions such as climbing obstacles, vaulting, stair traversal, jumping down, and continuously adapting to changing terrain. This work (links below) shows that terrain-aware whole-body motion generation can serve as an effective interface between perception and control. Rather than relying on fixed or terrain-agnostic behaviors, the system generates whole-body motion references online based on the observed terrain and navigation goal. These references are then tracked robustly on hardware using reinforcement learning. Implemented on the Unitree G1, the approach enables dynamic selection and seamless transitions between different locomotion styles (climbing, vaulting, stairs, jumping, and mixed terrain) while running fully onboard with real-time perception. Overall, the paper highlights a promising direction for humanoid locomotion: moving beyond flat-ground or reactive controllers toward systems that actively reason about the environment and generate suitable whole-body motions in real time. Thanks for sharing, Chenhao Li (@breadli428), and congrats on the amazing work by the rest of the team! 📌 Paper: arxiv.org/abs/2604.17335 Project Page: wholebodylocomotion.github.io Video: youtube.com/watch?v=OY61Ti… Share your new projects or papers with me, DMs are open! ——- Weekly robotics and AI insights. Subscribe free: 22astronauts.com
@willjhliang @bhawna_paliwal_ @HarithejaE @notmahi Very cool!
Random comments 1) not having a action bound in SAC is technically bad, 2) but having joint limits as action bounds for position control learning is also technically bad: the policy loses the ability to generate large torques near joint limits. Maybe torque is better for SAC.
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.
@leggedrobotics Thanks for this! I want to start looking into off policy methods for humanoids and this is very useful 🙏🏼
Super cool to see Tried using SAC for locomotion a few months ago and it kept breaking my head. The thing that got me was that asymmetric actor critic isn't equivalent across PPO and SAC. On policy the privileged critic magnifies the actors gradient. Off policy it sends it in a different direction entirely Most people would just accept SAC doesn't work here. Glad to see Big PPO finally getting antitrusted. Hopefully this sparks a revival of off policy algorithms
PPO has long dominated robot locomotion training in simulation. SAC, despite its sample efficiency, couldn't keep up. We analyze why: 🔗sabagian.github.io/sac_release_pr… 🔥Integrated into RSL-RL, our approach requires only minimal changes, making SAC a drop-in alternative out of the box.
@ChenTessler @JesseFarebro @leggedrobotics Totally agree
PPO has long dominated robot locomotion training in simulation. SAC, despite its sample efficiency, couldn't keep up. We analyze why: 🔗sabagian.github.io/sac_release_pr… 🔥Integrated into RSL-RL, our approach requires only minimal changes, making SAC a drop-in alternative out of the box.
This project is led by Gianluca Sabatini, supported by Chenhao Li @breadli428 and Marco Hutter @leggedrobotics. We thank Clemens Schwarke's implementation insights. github.com/leggedrobotics…
After many ablations, we left four important factors that make a difference ✅Right-sizing the action space ✅Treating timeouts as timeouts, not failures ✅Smoother targets via n-step returns ✅Starting exploration where it should Check out our findings arxiv.org/abs/2605.24975
Today, we bring SAC to RSL-RL, one of the most widely used RL frameworks in massively parallel robot learning, developed at RSL @leggedrobotics. We try to understand the long-standing performance gap between SAC and PPO, and crystallize important factors sabagian.github.io/sac_release_pr…
PPO has long dominated robot locomotion training in simulation. SAC, despite its sample efficiency, couldn't keep up. We analyze why: 🔗sabagian.github.io/sac_release_pr… 🔥Integrated into RSL-RL, our approach requires only minimal changes, making SAC a drop-in alternative out of the box.
Crazy! A new modality!
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