Marco Pavone(@drmapavone):Can an AI agent learn a pool table’s physics by playing on it? 🎱 Closing the visual gap between reality and simulation is one thing. Closing the dynamics gap is much harder. In this fun project, we let an agent take real shots, observe the outcomes, and iteratively improve its simulation of the physical world. The idea: rather than just observing the world, let agents actively experiment with it and learn its physics through interaction. A fun demonstration of a key capability for Physical AI: learning the physics of the real world through autonomous experimentation. A @corl_conf late breaking contribution. If you plan to attend #CoRL, see you there! In collaboration with @XilunZhangXZ @MatthewFoutter @Luis_A_Pabon @Liam862373 Check it out: https://t.co/aHpn6U8Sis #PhysicalAI #Robotics #AgenticAI #RobotLearning @StanfordAILab @StanfordASL

2026.11.08 22:21

Can an AI agent learn a pool table’s physics by playing on it? 🎱 Closing the visual gap between reality and simulation is one thing. Closing the dynamics gap is much harder. In this fun project, we let an agent take real shots, observe the outcomes, and iteratively improve its simulation of the physical world. The idea: rather than just observing the world, let agents actively experiment with it and learn its physics through interaction. A fun demonstration of a key capability for Physical AI: learning the physics of the real world through autonomous experimentation. A @corl_conf late breaking contribution. If you plan to attend #CoRL#, see you there! In collaboration with @XilunZhangXZ @MatthewFoutter @Luis_A_Pabon @Liam862373 Check it out: #PhysicalAI# #Robotics# #AgenticAI# #RobotLearning# @StanfordAILab @StanfordASL

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