  Long-horizon tasks, usually characterized by complex subtask dependencies,
present a significant challenge in manipulation planning. Skill chaining is a
practical approach to solving unseen tasks by combining learned skill priors.
However, such methods are myopic if sequenced greedily and face scalability
issues with search-based planning strategy. To address these challenges, we
introduce Generative Skill Chaining~(GSC), a probabilistic framework that
learns skill-centric diffusion models and composes their learned distributions
to generate long-horizon plans during inference. GSC samples from all skill
models in parallel to efficiently solve unseen tasks while enforcing geometric
constraints. We evaluate the method on various long-horizon tasks and
demonstrate its capability in reasoning about action dependencies, constraint
handling, and generalization, along with its ability to replan in the face of
perturbations. We show results in simulation and on real robot to validate the
efficiency and scalability of GSC, highlighting its potential for advancing
long-horizon task planning. More details are available at:
https://generative-skill-chaining.github.io/
