Robot Learning

Energy-Based Model (EBM)

Energy-Based Model (EBM) is a model that defines an unnormalized probability distribution through a scalar energy function, assigning low energy to compatible input configurations and high energy elsewhere. Inference is performed by minimizing energy or sampling, avoiding the need for a normalized likelihood. Implicit Behavioral Cloning (Florence et al., 2021) framed robot policies as EBMs over observation-action pairs, capturing multimodal and discontinuous action distributions that regression smooths over.

Why it matters for physical AI

Multimodal demonstrations, where several actions are equally valid, break mean-regression policies. Energy-based and diffusion policies were the two responses that reshaped imitation learning architectures for manipulation.