  Imitation Learning (IL) is a sample efficient paradigm for robot learning
using expert demonstrations. However, policies learned through IL suffer from
state distribution shift at test time, due to compounding errors in action
prediction which lead to previously unseen states. Choosing an action
representation for the policy that minimizes this distribution shift is
critical in imitation learning. Prior work propose using temporal action
abstractions to reduce compounding errors, but they often sacrifice policy
dexterity or require domain-specific knowledge. To address these trade-offs, we
introduce HYDRA, a method that leverages a hybrid action space with two levels
of action abstractions: sparse high-level waypoints and dense low-level
actions. HYDRA dynamically switches between action abstractions at test time to
enable both coarse and fine-grained control of a robot. In addition, HYDRA
employs action relabeling to increase the consistency of actions in the
dataset, further reducing distribution shift. HYDRA outperforms prior imitation
learning methods by 30-40% on seven challenging simulation and real world
environments, involving long-horizon tasks in the real world like making coffee
and toasting bread. Videos are found on our website:
https://tinyurl.com/3mc6793z
