Approaching the block, avoiding the block
A robot receives a different goal while the world in front of it stays the same. In the AnyGoal study’s Push-T example, training trajectories lead the robot toward the block it must push. Avoid-T instead requires familiar two-dimensional movement without hitting the block. There is no new motor or physical law. What changes is the situation in which a familiar movement is requested. Heejeong Nam and colleagues consequently ask an interesting question: has the model learned the effect of the supplied action, or what usually happens next in expert demonstrations? Both explanations can predict the same image on the training task.[1]
The useful contribution, to my mind, is unpacking the word familiar. Positions may each have been observed and actions may each have occurred, while a particular action at a particular position remains missing. AnyGoal targets new combinations of components covered by training. It provides 8 variants derived from 4 base tasks, with 400 episodes per variant. Checking that individual factors remain inside training coverage makes a completely different physical environment a less plausible explanation for failure. It does not claim that every joint configuration is familiar. That joint gap is precisely the object of measurement.[1]
The difficulty that remains without images
Visual input adds another explanation. A new combination of familiar objects and positions may produce an image the model has never encountered as a whole. Difficulty can then begin in representation before the action rule is even considered. The researchers bypass that stage for some predictors by supplying the true task-relevant state directly. Planning can remain stable in free-space variants while deterioration persists in contact-rich variants. The finding is therefore more informative than one general failure rate: visual representation and learning state transitions cannot be treated as the same obstacle.[1]
This comparison does not establish that every error comes from imitating expert behavior. Contact-rich movement may demand more precise prediction: free space can offer many routes to the goal, while correctly pushing an object may require a narrower sequence. The paper also reports that prediction error and planning success can diverge on some tasks. A small visual error can matter to the task, while an imperfect intermediate path can still reach its goal. Reading both outcomes together is consequently more informative than treating either as a complete account of capability.[1]
Changing the action, holding the world fixed
Another experiment holds the state fixed and changes the action supplied to the model. If the prediction barely changes, that provides a signal that the model may be following a habitual continuation in that region. The authors report reduced sensitivity where expert behavior is tightly constrained by the training goal. Low sensitivity alone does not establish an incorrect result: some actions can genuinely have similar consequences. The value of the intervention is asking how much the supplied command contributes to prediction, rather than judging a continuation only by how convincing its video looks.[1]
AnyGoal remains a preprint and does not establish reliability beyond its selected simulated tasks. It nevertheless gives researchers a useful distinction: does an error under a new goal arise from the perceived state, the learned action relationship, or the task structure reflected in the planning metric? Comparing different actions under a fixed state, and direct state information within the same task, helps separate those explanations. Reuse in a world model thereby becomes a concrete question about composition: can it apply known components where it has not previously encountered them together?[1]