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#Robot Manipulation

Chain of World: World Model Thinking in Latent Motion

Fuxiang Yang, Donglin Di et al.Mar 3arXiv

Robots learn better when they think about how things move over time, not by redrawing every pixel of a video.

#Vision-Language-Action#World Model#Latent Motion

GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning

GigaBrain Team, Boyuan Wang et al.Feb 12arXiv

GigaBrain-0.5M* is a robot brain that sees, reads, and acts, and it gets smarter by imagining the future before moving.

#Vision-Language-Action#World Model#Reinforcement Learning

RISE: Self-Improving Robot Policy with Compositional World Model

Jiazhi Yang, Kunyang Lin et al.Feb 11arXiv

RISE lets a robot learn safely and cheaply by practicing in its imagination instead of always in the real world.

#Reinforcement Learning#World Models#Compositional World Model

VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer

Songqiao Hu, Zeyi Liu et al.Dec 9arXiv

Robots that follow pictures and words (VLA models) can do many tasks, but they often bump into things because safety isn’t guaranteed.

#Vision-Language-Action#Safety Constraint Layer#Control Barrier Function