A Value Function Space Approach for Hierarchical Planning with Signal Temporal Logic Tasks

Abstract

Signal Temporal Logic (STL) has emerged as an expressive language for reasoning intricate planning objectives. However, existing STL-based methods often assume full observation and known dynamics, which imposes constraints on real-world applications. To address this challenge, we propose a hierarchical planning framework that starts by constructing the Value Function Space (VFS) for state and action abstraction, which embeds functional information about affordances of the low-level skills. Subsequently, we utilize a neural network to approximate the dynamics in the VFS and employ sampling based optimization to synthesize high-level skill sequences that maximize the robustness measure of the given STL tasks in the VFS. Then those skills are executed in the low-level environment. Empirical evaluations in the Safety Gym and ManiSkill environments demonstrate that our method accomplish the STL tasks without further training in the low-level environments, substantially reducing the training burdens.

References in corpus (4)

• STLCCP: Efficient Convex Optimization-based Framework for Signal Temporal Logic Specifications
• Controller Synthesis of Collaborative Signal Temporal Logic Tasks for Multi-Agent Systems via Assume-Guarantee Contracts
• Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning
• Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning