# Solving Physics Puzzles by Reasoning about Paths

arXiv: https://arxiv.org/abs/2011.07357 · HTML: https://mlsanity.com/paper/2011.07357/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines](https://mlsanity.com/paper/2411.08027/) (2024)
- [UniTeam: Open Vocabulary Mobile Manipulation Challenge](https://mlsanity.com/paper/2312.08611/) (2023)
- [Critic Guided Segmentation of Rewarding Objects in First-Person Views](https://mlsanity.com/paper/2107.09540/) (2021)
- [Towards Open-World Mobile Manipulation in Homes: Lessons from the Neurips 2023 HomeRobot Open Vocabulary Mobile Manipulation Challenge](https://mlsanity.com/paper/2407.06939/) (2024)
- [Planning with RL and episodic-memory behavioral priors](https://mlsanity.com/paper/2207.01845/) (2022)
- [Benchmarks for Physical Reasoning AI](https://mlsanity.com/paper/2312.10728/) (2023)
- [Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space](https://mlsanity.com/paper/2207.01275/) (2022)
- [Towards robust and domain agnostic reinforcement learning competitions](https://mlsanity.com/paper/2106.03748/) (2021)
- [Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel](https://mlsanity.com/paper/2505.22861/) (2025)

## Abstract (arXiv)

We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular structure is motivated by hypothesizing a sequence of intuitive steps that humans apply when trying to solve such a task. The model first predicts the path the target object would follow without intervention and the path the target object should follow in order to solve the task. Next, it predicts the desired path of the action object and generates the placement of the action object. All components of the model are trained jointly in a supervised way; each component receives its own learning signal but learning signals are also backpropagated through the entire architecture. To evaluate the model we use PHYRE - a benchmark test for goal-driven physical reasoning in 2D mechanics puzzles.