Partial Joint Correction of Abnormal Motion Data via Reward Function Design in Virtual Environments

Most reinforcement learning-based humanoid motion studies emphasize full-body imitation, limiting selective correction of abnormal joints. This study proposes a reward function design that corrects abnormal joint behavior while preserving motion style, using two approaches: periodic positional targets and pre-trained joint angle references. Applied separately, both guided the agent to recover natural swing motion. Experiments in a physics-based simulation showed improved joint mobility and corrected gait patterns. The results highlight that targeted correction is achievable with imperfect motion data through reward design alone, with potential applications in rehabilitation simulations and user-feedback systems in virtual environments.