# Physics-Based Task Generation through Causal Sequence of Physical Interactions

arXiv: https://arxiv.org/abs/2308.02835 · HTML: https://mlsanity.com/paper/2308.02835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [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)
- [PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning](https://mlsanity.com/paper/2112.05136/) (2021)
- [Phy-Q as a measure for physical reasoning intelligence](https://mlsanity.com/paper/2108.13696/) (2021)
- [ChatGPT4PCG Competition: Character-like Level Generation for Science Birds](https://mlsanity.com/paper/2303.15662/) (2023)
- [The 2017 AIBIRDS Competition](https://mlsanity.com/paper/1803.05156/) (2018)
- [Deceptive Level Generation for Angry Birds](https://mlsanity.com/paper/2106.01639/) (2021)
- [Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds](https://mlsanity.com/paper/1902.02518/) (2019)
- [NovPhy: A Testbed for Physical Reasoning in Open-world Environments](https://mlsanity.com/paper/2303.01711/) (2023)

## Abstract (arXiv)

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.