Physics-Based Task Generation through Causal Sequence of Physical Interactions

TL;DR

This paper introduces a systematic method for generating physics-based tasks from causal sequences of interactions, aiding in evaluating and developing AI systems' physical reasoning in simulated environments.

Contribution

The paper presents a novel approach to task generation in physics simulations using causal interaction sequences, enhancing the assessment of physical reasoning capabilities.

Findings

Successfully applied to Angry Birds puzzle game

Generated tasks evaluated for stability and solvability

Facilitates nuanced evaluation of physical reasoning agents

Abstract

Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.