✅ Causal-PIK outperforms state-of-the-art baselines, requiring fewer attempts to solve the tasks.
✅ High correlation in scores between humans and Causal-PIK suggests high alignment in the types of physical dynamics that were found to be easy or difficult to reason about.

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📊 We evaluate our method on Virtual Tools and PHYRE, benchmarks designed to evaluate agents on single-intervention tasks that involve complex physical interactions between objects.

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2️⃣ Smart action selection - Causal-PIK separates the direct effects of an action from confounding factors arising from the environment, allowing informed decision-making.

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1️⃣ Learning from fewer examples — Causal-PIK generalizes from a single experience to multiple actions by predicting which ones will lead to similar outcomes.

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🔍 How can we build AI agents that reason about the physical world the way humans do (or better) ?

Excited to share Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel, which will be presented next Thursday July 17th at ICML in Vancouver!

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