Policy-Dependent View Acquisition

Selecting camera frames before transmission requires a robot to estimate the value of observations it has not yet received. That value depends on both the views and the policy that consumes them. We formulate conditional view-set value and distinguish decision-level interaction from excess interaction induced by a policy. Selective substitutability training (SST) penalizes excess interaction together with subset regret and action-uncertainty mismatch; a residual coalition scheduler (RCS) selects fresh views from low-rate camera sketches under a per-decision byte cap. Across eight MuJoCo/robosuite manipulation tasks and three training seeds, SST+RCS achieves 69.58% episode success at a 55% communication cap, compared with 62.92% for all-subset training with a Set Transformer. The paired difference is 6.67 percentage points, with a 95% hierarchical bootstrap interval of [2.78,10.56]. A separate eight-seed evaluation shows a similar intermediate-budget gain. An expanded physical evaluation yields a +7.81-point paired difference across 576 trials per method. In the main frontier, SST+RCS loses at the smallest cap, ties at full view and has slightly higher mean latency than the reference.