Give PragyaDex a natural-language manipulation goal. The system constructs a physical context, proposes dexterity-program tokens, searches contact- and object-state-valid futures, retargets the winning program to the robot embodiment, and executes one verified transition at a time.
The observation is grounded in the same robot, bottle, cup, and hand configuration used by the manipulation rollout. Contact and object state are then extracted as structured planning variables.
The decoder does not emit a bare verb. Each candidate binds a primitive to hand roles, contact additions/removals, object-state effect, contact geometry, continuous parameters, and duration.
The proposed planner separates model likelihood, physical-prefix admissibility, robot feasibility, and terminal program quality through a lazy diverse beam with increasingly expensive checks.
Cheap symbolic/contact checks cover the large conceptual search. Retargeting and physics are invoked only on promising prefixes.
Inspect the same winning program simultaneously as physical motion, primitive sequence, bimanual roles, contact evolution, object-state evolution, likelihood, and terminal quality.
τ* = [d₁*, d₂*, d₃*, d₄*, d₅*]
01STABILIZE
02GRASP CAP
03UNSCREW
04REGRASP
05POUR
STABILIZE → GRASP CAP → UNSCREW → REGRASP → POUR
Retargeting is treated as an optimization problem: preserve task-relevant pose and contact structure while satisfying robot joint limits and collision constraints.

The selected five-token program is not executed open-loop. PragyaDex commits one token, measures contact and object evidence, updates the planner state, and regenerates the frontier.