AMO:
Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control

- framework that integrates sim-to-real reinforcement learning (RL) with trajectory optimization for real-time, adaptive whole-body control

#RSS2025 #humanoid

X overview, Project Website

🧵 Thread • FixupX

1:39

Xuxin Cheng (@xuxin_cheng)

Meet 𝐀𝐌𝐎 — our universal whole‑body controller that unleashes the 𝐟𝐮𝐥𝐥  kinematic workspace of humanoid robots to the physical world.
AMO is a single policy trained with RL + Hybrid Mocap & Trajectory‑Opt.

Accepted to #RSS2025.
Try our open models & more…

Unified World Models: Coupling Video and Action Diffusion
for Pretraining on Large Robotic Datasets

- combine video and action diffusion in one transformer, can be train from robot trajectories and action-free videos.

- represents a policy, a forward dynamics model, an inverse dynamics model, and a video prediction model in a unified framework.

#RSS2025 #diffusion
X overview, Project Website

🧵 Thread • FixupX

Chuning Zhu (@chuning_zhu)

Scaling imitation learning has been bottlenecked by the need for high-quality robot data, which are expensive to collect. But are we utilizing existing data to the fullest extent? A thread (1/11)

HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots

- universal controller for humanoids utilizing co-train with mask
- detailed benchmark

#RSS2025 #humanoid

X Overview, Project Website

🧵 Thread • FxTwitter / FixupX

Tairan He (@TairanHe99)

🤖 Humanoids are expected to be the ultimate embodiment for general-purpose robots, but reaching that potential means adapting to diverse tasks—navigation, tabletop manipulation, loco-manipulation—each needing unique control modes.

🤔 Can one controller handle…

Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation

- good engineering using AR
- slow+fast diffusion procedure

#RSS2025 #contact_rich #tactile

X Overview, Project Website

🧵 Thread • FixupX

1:40

Han Xue (@HanXue012)

Humans can easily perform complex contact-rich tasks with vision and touch, but these tasks remain challenging for robots. How can we resolve this from both the algorithm side and the data side?

Introducing Reactive Diffusion Policy (RDP),a slow-fast imitation…