Representation World Model: Learning States, Transition and Executable Plans in Representation
- Published
- Source
- arXiv
- Paper number
- 1123
- Field
- Robotics
- arXiv ID
- 2609.29171
Key points
- Proposed a world model that learns states, transitions, and plans together within a single representation space
- Trained the representation so latent paths become actually executable plans, using inverse-dynamics supervision along the path
- Completed planning at inference time purely by path construction, without recursive rollouts or action-space search
- Achieved an average success rate of 93% on the LIBERO-Goal robot manipulation benchmark, far ahead of LeWM (71.2%)
- Outperformed the strongest search-based baseline (which evaluates 384 candidate trajectories) with no search at all
Paper links
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