# Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel

arXiv: https://arxiv.org/abs/2505.22861 · HTML: https://mlsanity.com/paper/2505.22861/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 17 of 17)

- [PHYRE: A New Benchmark for Physical Reasoning](https://mlsanity.com/paper/1908.05656/) (2019)
- [Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics](https://mlsanity.com/paper/1602.04450/) (2016)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [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)
- [Learning Long-term Visual Dynamics with Region Proposal Interaction Networks](https://mlsanity.com/paper/2008.02265/) (2020)
- [Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization](https://mlsanity.com/paper/1703.01250/) (2017)
- [Bayesian Optimization Meets Riemannian Manifolds in Robot Learning](https://mlsanity.com/paper/1910.04998/) (2019)
- [Interpretable Intuitive Physics Model](https://mlsanity.com/paper/1808.10002/) (2018)
- [Forward Prediction for Physical Reasoning](https://mlsanity.com/paper/2006.10734/) (2020)
- [Deep Kernels for Optimizing Locomotion Controllers](https://mlsanity.com/paper/1707.09062/) (2017)
- [On the Learning Mechanisms in Physical Reasoning](https://mlsanity.com/paper/2210.02075/) (2022)
- [Bayesian Optimization in Variational Latent Spaces with Dynamic Compression](https://mlsanity.com/paper/1907.04796/) (2019)
- [Solving Physics Puzzles by Reasoning about Paths](https://mlsanity.com/paper/2011.07357/) (2020)
- [PIP: Physical Interaction Prediction via Mental Simulation with Span Selection](https://mlsanity.com/paper/2109.04683/) (2021)
- [Benchmarks for Physical Reasoning AI](https://mlsanity.com/paper/2312.10728/) (2023)
- [Physical Reasoning Using Dynamics-Aware Models](https://mlsanity.com/paper/2102.10336/) (2021)

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

- [Moving Out: Physically-grounded Human-AI Collaboration](https://mlsanity.com/paper/2507.18623/) (2025)
- [Learning Robust Visuomotor Policies via Dynamics Alignment](https://mlsanity.com/paper/2510.27114/) (2025)

## Abstract (arXiv)

Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult. These tasks require agents to iteratively improve their actions after actively exploring causes and effects in the environment. For these type of tasks, we propose Causal-PIK, a method that leverages Bayesian optimization to reason about causal interactions via a Physics-Informed Kernel to help guide efficient search for the best next action. Experimental results on Virtual Tools and PHYRE physical reasoning benchmarks show that Causal-PIK outperforms state-of-the-art results, requiring fewer actions to reach the goal. We also compare Causal-PIK to human studies, including results from a new user study we conducted on the PHYRE benchmark. We find that Causal-PIK remains competitive on tasks that are very challenging, even for human problem-solvers.