Object-Centric Unsupervised Skill Discovery with Action-Influence Weighting

Unsupervised skill discovery learns reusable behaviors without extrinsic task rewards. In multi-object environments, however, conventional skill representations typically encode behavior in a global state space, leaving unspecified which object a skill is intended to affect. This ambiguity can cause skill learning to favor easily induced changes in the agent or other state factors rather than meaningful changes in a target object. We address this problem with an object-centric skill representation $\zeta =(g,z)$, where the discrete variable $g$ selects a target object and $z$ specifies its continuous skill vector. Furthermore, we weight the object-level intrinsic reward by a causal action influence, which quantifies the conditional influence of actions on the target object's next-state distribution given the full current state. The resulting intrinsic objective focuses distance-maximizing skill learning on target-object state changes that are more controllable by the agent. Experiments across multiple object-interaction environments show that the proposed method improves object-state-space coverage, target-object adherence, and the utility of learned skills for downstream control compared with existing unsupervised skill discovery methods.