NovPhy: A Testbed for Physical Reasoning in Open-world Environments

TL;DR

NovPhy is a new testbed designed to evaluate and develop AI agents' physical reasoning and adaptability in open-world environments with novel situations, highlighting current performance gaps between humans and AI.

Contribution

The paper introduces NovPhy, a comprehensive testbed for assessing physical reasoning and adaptability to novelties in open-world scenarios, with diverse tasks and evaluation metrics.

Findings

Humans outperform AI agents in physical reasoning and adaptability.

AI agents struggle to adapt quickly to novelties in physical scenarios.

Agents with adaptability capabilities perform worse under novelty conditions than humans.

Abstract

Due to the emergence of AI systems that interact with the physical environment, there is an increased interest in incorporating physical reasoning capabilities into those AI systems. But is it enough to only have physical reasoning capabilities to operate in a real physical environment? In the real world, we constantly face novel situations we have not encountered before. As humans, we are competent at successfully adapting to those situations. Similarly, an agent needs to have the ability to function under the impact of novelties in order to properly operate in an open-world physical environment. To facilitate the development of such AI systems, we propose a new testbed, NovPhy, that requires an agent to reason about physical scenarios in the presence of novelties and take actions accordingly. The testbed consists of tasks that require agents to detect and adapt to novelties in physical scenarios. To create tasks in the testbed, we develop eight novelties representing a diverse novelty space and apply them to five commonly encountered scenarios in a physical environment. According to our testbed design, we evaluate two capabilities of an agent: the performance on a novelty when it is applied to different physical scenarios and the performance on a physical scenario when different novelties are applied to it. We conduct a thorough evaluation with human players, learning agents, and heuristic agents. Our evaluation shows that humans' performance is far beyond the agents' performance. Some agents, even with good normal task performance, perform significantly worse when there is a novelty, and the agents that can adapt to novelties typically adapt slower than humans. We promote the development of intelligent agents capable of performing at the human level or above when operating in open-world physical environments. Testbed website: https://github.com/phy-q/novphy