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

arXiv: https://arxiv.org/abs/2303.01711 · HTML: https://mlsanity.com/paper/2303.01711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 19 of 19)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [CLEVRER: CoLlision Events for Video REpresentation and Reasoning](https://mlsanity.com/paper/1910.01442/) (2019)
- [Physion: Evaluating Physical Prediction from Vision in Humans and Machines](https://mlsanity.com/paper/2106.08261/) (2021)
- [PHYRE: A New Benchmark for Physical Reasoning](https://mlsanity.com/paper/1908.05656/) (2019)
- [CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning](https://mlsanity.com/paper/2010.04296/) (2020)
- [CoPhy: Counterfactual Learning of Physical Dynamics](https://mlsanity.com/paper/1909.12000/) (2019)
- [IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning](https://mlsanity.com/paper/1803.07616/) (2018)
- [Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning](https://mlsanity.com/paper/1907.09620/) (2019)
- [The AGI Containment Problem](https://mlsanity.com/paper/1604.00545/) (2016)
- [NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty](https://mlsanity.com/paper/2203.12117/) (2022)
- [Phy-Q as a measure for physical reasoning intelligence](https://mlsanity.com/paper/2108.13696/) (2021)
- [Detecting and Adapting to Novelty in Games](https://mlsanity.com/paper/2106.02204/) (2021)
- [A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels](https://mlsanity.com/paper/2011.12906/) (2020)
- [The 2017 AIBIRDS Competition](https://mlsanity.com/paper/1803.05156/) (2018)
- [NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds](https://mlsanity.com/paper/2206.11736/) (2022)
- [The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds](https://mlsanity.com/paper/2106.08670/) (2021)
- [Physical Reasoning in an Open World](https://mlsanity.com/paper/2201.08950/) (2022)

## Built on by (most cited first, 2 of 2)

- [Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models](https://mlsanity.com/paper/2311.09064/) (2023)
- [Physics-Based Task Generation through Causal Sequence of Physical Interactions](https://mlsanity.com/paper/2308.02835/) (2023)

## Abstract (arXiv)

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