Quantitative trajectory analysis of VLLR behaviors

Conduct a quantitative analysis of robot trajectories to characterize how VLLR’s self-certainty intrinsic reward and VLM-based value initialization respectively influence action decisiveness, alignment with the subgoal decomposition, optimization speed, and task efficiency.

Background

The paper presents qualitative observations that self-certainty improves internal consistency and convergence speed, while VLM-derived value initialization enhances global task efficiency through semantic grounding.

To substantiate these qualitative findings, the authors indicate that a systematic, quantitative trajectory analysis has not yet been performed and is deferred to future work.