TUM team surveys 329 works on physics-embedded robot learning: 71% encode physics in architectures, only 4% combine multiple embedding routes
This survey systematically reviews methods that embed physics priors into robot learning and proposes a unified taxonomy classifying existing work by where physics is embedded—physics-guided inputs, data, and representations; physics-encoded model architectures; and physics-informed training losses—then reviews methods for robot dynamics learning, trajectory planning and prediction, control, and estimation together with the open-source software ecosystem, reporting that physics-encoded architectures account for 71% of surveyed methods, physics-guided 16%, and physics-informed 13%, while only 4% combine more than one embedding route.