WAM
A robot control paradigm that performs planning and decision-making by learning an internal world model of how the environment changes in response to actions. It emphasizes modeling the causal relationships of the physical world to achieve stronger generalization capabilities and few-shot learning.
Timeline (last 90 days)
WAM proponents argue that once predictive models are sufficiently accurate, agents can achieve few-shot or even zero-shot motion planning through model predictive control (MPC) or latent-space tree search, bypassing the reliance on large volumes of real demonstration data.
VLA leans toward end-to-end imitation and builds capability through data scale, while WAM leans toward explicit world modeling and improves generalization through structured reasoning.
LIBERO simulation successfully runs long-horizon tasks on Mac for the first time, with GR00T achieving a 100% success rate, WAM achieving 98%, and a 2.8-billion-parameter VLA small model achieving 96%.
All Facts (3)
WAM proponents argue that once predictive models are sufficiently accurate, agents can achieve few-shot or even zero-shot motion planning through model predictive control (MPC) or latent-space tree search, bypassing the reliance on large volumes of real demonstration data.
50%UnverifiedVLA leans toward end-to-end imitation and builds capability through data scale, while WAM leans toward explicit world modeling and improves generalization through structured reasoning.
50%UnverifiedLIBERO simulation successfully runs long-horizon tasks on Mac for the first time, with GR00T achieving a 100% success rate, WAM achieving 98%, and a 2.8-billion-parameter VLA small model achieving 96%.
50%