Symbolic task representation is a powerful tool for encoding human instructions and domain knowledge. Such instructions guide robots to accomplish diverse objectives and meet constraints through reinforcement learning (RL). Most existing methods are based on fixed mappings from environmental states to symbols. However, in inspection tasks, where equipment conditions must be evaluated from multiple perspectives to avoid errors of oversight, robots must fulfill the same symbol from different states. To help robots respond to flexible symbol mapping, we propose representing symbols and their mapping specifications separately within an RL policy. This approach imposes on RL policy to learn combinations of symbolic instructions and mapping specifications, requiring an efficient learning framework. To cope with this issue, we introduce an approach for learning flexible policies called Symbolic Instructions with Adjustable Mapping Specifications (SIAMS). This paper represents symbolic instructions using linear temporal logic (LTL), a formal language that can be easily integrated into RL. Our method addresses the diversified completion patterns of instructions by (1) a specification-aware state modulation, which embeds differences in mapping specifications in state features, and (2) a symbol-number-based task curriculum, which gradually provides tasks according to the learning's progress. Evaluations in 3D simulations with discrete and continuous action spaces demonstrate that our method outperforms context-aware multitask RL comparisons.

Devising a strategy to make a system mimic behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of ltlf , a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, DA and DB, and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of DA into properties on behaviors of DB. The goal is to synthesize a strategy that step-by-step maps every behavior of DA into a behavior of DB so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf , and for each, we study synthesis algorithms and computational properties.