LeWAM: Robot planning method based on JEPA world action model and diffusion-guided MPC

The authors propose LeWAM, a bidirectional Transformer trained end-to-end on a decoder-free JEPA latent space that supports four modes: forward, backward, inverse dynamics, and policy prediction. Linear probe experiments show that LeWAM's latent space reads robot and object state better than the forward-only Le World Model, and that its degree of ignoring visual distractors is comparable to LeWM and clearly better than reconstruction-based WAM.