DDOI: Decomposed Skill Discovery for Object Interaction

While unsupervised skill discovery based on mutual information (MI) has shown promise for learning reusable robot behaviors, existing methods often discover static skills with limited state coverage. This can limit their usefulness in robot–object interaction settings where meaningful object-state transitions are difficult to induce through the robot’s motion. In this work, we propose DDOI (Decomposed Skill Discovery for Object Interaction) for a robot agent interacting with an object. Under the MI-based skill discovery framework, the latent skill is decomposed into an object skill and a robot skill. The object skill is learned via an object skill discriminator constrained by either a Euclidean or controllability-aware distance, encouraging far-reaching and hard-to-achieve transitions in object states. The robot skill is trained through a robot skill discriminator that conditions on both robot and object states as well as the object skill, enabling the robot to acquire behaviors that help realize desired object-state transitions. We tested DDOI in Ant, Ant-Box, and Humanoid MuJoCo environments as planar single-object interaction benchmarks, using object-state coverage as the main evaluation metric and downstream goal-reaching success rates as an additional metric. Across these environments, DDOI variants demonstrated more consistent object-state coverage and stronger downstream performance than the compared baselines.