Tactile Memory for Continuous Policy Blending in Unified Force-Impedance Control

Abstract

As of today, automating contact-rich industrial manipulation processes, such as insertion, plugging, and screw-driving, is tedious and requires expert knowledge. The processes consist of programmable, common action units, like moving to a pose and establishing contact. However, the user still has to decide on fixed transition conditions to successfully complete each sub-action. Instead, we introduce a tactile memory-driven policy blending framework based on unified force-impedance control to transition autonomously. At the core of our approach lies a structured representation of manipulation as a sequence of basic operations combined into any relevant process, each governed by real-time sensory feedback and annotated with process quality metrics (PQMs), capturing motion, force, and energy-level interactions. A bidirectional long-short term memory model (BiLSTM) encodes the recent PQM's histories to determine basic operation success. Later, soft blending weights are generated, allowing smooth, adaptive transitions between operations without manual phase definition. To ensure functional safety during contact, we integrate an energy tank mechanism that enforces passivity by regulating energy exchange. The resulting control scheme enables robust and continuous tactile manipulation across variations in object geometry and spatial configurations. Experimental validation across four processes over five objects and two position variants demonstrates successful transfer and resilience to position disturbances. Our findings highlight that learned tactile memory and quality feedback embedded in the control loop serve as a principled foundation for intelligent and transferable manipulation, allowing fully autonomous process planning and execution in the future.