Symbolic Manipulation Planning with Discovered Object and Relational Predicates

TL;DR

This paper presents a system that autonomously discovers object and relational symbols from a robot's exploration, learns rules, converts them into PDDL, and plans complex tasks involving multiple objects and their relations, improving planning performance.

Contribution

The work introduces a novel approach for learning symbolic rules from continuous sensorimotor data, enabling scalable planning with arbitrary object relations.

Findings

Successfully learned symbolic representations of object interactions.

Generated effective plans involving multiple objects and relations.

Outperformed state-of-the-art methods in planning tasks.

Abstract

Discovering the symbols and rules that can be used in long-horizon planning from a robot's unsupervised exploration of its environment and continuous sensorimotor experience is a challenging task. The previous studies proposed learning symbols from single or paired object interactions and planning with these symbols. In this work, we propose a system that learns rules with discovered object and relational symbols that encode an arbitrary number of objects and the relations between them, converts those rules to Planning Domain Description Language (PDDL), and generates plans that involve affordances of the arbitrary number of objects to achieve tasks. We validated our system with box-shaped objects in different sizes and showed that the system can develop a symbolic knowledge of pick-up, carry, and place operations, taking into account object compounds in different configurations, such as boxes would be carried together with a larger box that they are placed on. We also compared our method with the state-of-the-art methods and showed that planning with the operators defined over relational symbols gives better planning performance compared to the baselines.