  Offline planning often struggles with poor sampling efficiency as it tries to
learn policies from scratch. Especially with diffusion models, such cold start
practices mean that both training and sampling become very expensive. We
hypothesize that certain environment constraint priors or cheaply available
policies make it unnecessary to learn from scratch, and explore a way to
incorporate such priors in the learning process. To achieve that, we borrow a
variation of the Schr\"odinger bridge formulation from the image-to-image
setting and apply it to planning tasks. We study the performance on some
planning tasks and compare the performance against the DDPM formulation. The
code for this work is available at
https://github.com/adrshsrvstv/bridge_diffusion_planning.
