Physical Commonsense

Physical commonsense reasoning in artificial intelligence focuses on enabling machines to understand and predict the physical interactions of objects in the world, mirroring human intuitive understanding. Current research emphasizes developing and evaluating models' abilities to generate physically plausible images and videos from textual descriptions, using benchmarks that assess performance across various physical scenarios (mechanics, optics, thermodynamics). This research is crucial for advancing AI capabilities in areas like robotics, virtual/augmented reality, and safe human-computer interaction, as it addresses the significant gap between current AI performance and human-level physical intuition. Multimodal approaches, incorporating both visual and auditory information, are increasingly recognized as essential for achieving robust physical commonsense reasoning.