Learning Manipulation-Sufficient Representations via Outcome Bottlenecks
Networked manipulation endpoints couple perception to actuation across compute- and bandwidth-limited links, yet commonly exchange dense geometric states optimized for fidelity rather than action outcomes. A stochastic representation is learned with a policy-free, action-conditioned outcome bottleneck: marginal outcome log-loss supplies distortion and a KL term regularizes rate. The construction is motivated by the minimal statistic that preserves the outcome distribution of every admissible action, while the implemented finite model is evaluated as a rate-regularized mixture predictor. The same encoder and outcome head support grasp selection, singleton conformal filtering, active viewpo...
| Authors | Md Selim Sarowar, Sungho Kim |
|---|---|
| Primary category | cs.RO |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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