Touch as Progress: Residual Policy for Precision Robot Manipulation

Tactile feedback provides direct access to subtle physical interactions and is an important sensing modality for precision manipulation. Similar tactile signals may occur at different stages of an interaction and call for different actions, leaving the underlying interaction state ambiguous from individual contact observations alone. This work presents Tactile Progress Residual (TPR), which represents the interaction process as progress estimated from recent tactile and robot-state observations and uses it to organize residual correction of base-policy actions. The progress estimator is trained with weak temporal supervision from recorded interactions without manual stage annotations. The estimated progress conditions a lightweight residual policy, provides process-level feedback for learning through progress differences, and determines when residual correction is applied. Conditioned on progress, the residual policy combines tactile feedback with base-policy predictions to generate bounded action corrections. Experiments on ManiFeel, ManiSkill3, and real-world precision manipulation tasks show that TPR consistently outperforms strong vision-only and tactile baselines. Further ablations validate the effectiveness of tactile-based progress estimation and the role of progress in organizing residual correction. Code is released at an anonymous repository.