arXiv
Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience
World models enable embodied agents to anticipate action consequences, yet jointly modeling locomoti...
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arXiv
CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in const...
arXiv
Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy
In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of...
arXiv
StopWatch: Goal Grounding for Object-goal Navigation
Pretrained VLMs bring rich world knowledge and semantic priors to object-goal navigation, and fine-t...
arXiv
Self-Building Labs
Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental executio...
arXiv
LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation
Personalization is essential for general-purpose robots to transition into household environments an...
arXiv
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
Predicting how the world responds to an action requires more than what can be seen: robots must acco...
arXiv
Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation
We introduce P$^{3}$-WAM, a unified world action model for egocentric dexterous manipulation that le...
arXiv
CoDA: Co-Evolution of Data Engines and Action Policies
Generative simulation can automate robot environment construction, but physically valid environments...
arXiv
GeoState: Learning View-Decoupled Geometry States for 3D World Modeling
Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictiv...
arXiv
Dexterity from Supervised Play
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulati...
arXiv
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion
Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasin...
arXiv
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tunin...
arXiv
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks
It is difficult to accurately model the physics of the interaction between a robot and the objects i...
arXiv
ChunkAct: Action Chunking for Agentic Robotics
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it compo...