Real-Time Verification of Embodied Reasoning for Generative Skill Acquisition (2505.11175v2)

Abstract: Generative skill acquisition enables embodied agents to actively learn a scalable and evolving repertoire of control skills, crucial for the advancement of large decision models. While prior approaches often rely on supervision signals from generalist agents (e.g., LLMs), their effectiveness in complex 3D environments remains unclear; exhaustive evaluation incurs substantial computational costs, significantly hindering the efficiency of skill learning. Inspired by recent successes in verification models for mathematical reasoning, we propose VERGSA (Verifying Embodied Reasoning in Generative Skill Acquisition), a framework that systematically integrates real-time verification principles into embodied skill learning. VERGSA establishes 1) a seamless extension from verification of mathematical reasoning into embodied learning by dynamically incorporating contextually relevant tasks into prompts and defining success metrics for both subtasks and overall tasks, and 2) an automated, scalable reward labeling scheme that synthesizes dense reward signals by iteratively finalizing the contribution of scene configuration and subtask learning to overall skill acquisition. To the best of our knowledge, this approach constitutes the first comprehensive training dataset for verification-driven generative skill acquisition, eliminating arduous manual reward engineering. Experiments validate the efficacy of our approach: 1) the exemplar task pool improves the average task success rates by 21%, 2) our verification model boosts success rates by 24% for novel tasks and 36% for encountered tasks, and 3) outperforms LLM-as-a-Judge baselines in verification quality.

Real-Time Verification of Embodied Reasoning for Generative Skill Acquisition: A Critical Overview

The paper "Real-Time Verification of Embodied Reasoning for Generative Skill Acquisition" presents the VERGSA framework, which aims to enhance the efficiency and efficacy of generative skill acquisition in embodied agents. Embodied AI, particularly focused on empowering agents to autonomously learn and refine skills in dynamic environments, stands to benefit significantly from the proposed verification model integrated within this framework.

Embodied learning challenges extend beyond those found in traditional mathematical problem-solving due to the less structured nature of physical environments and the complexity of defining tasks and subtasks therein. The VERGSA framework introduces real-time verification mechanisms to address these challenges. It does so by dynamically incorporating contextually relevant tasks into learning prompts and employing a scalable automated reward labeling scheme. This procedural innovation synthesizes dense reward signals, contributing to the reduction of exhaustive manual annotation processes previously necessary in these domains.

The empirical evaluations presented in the paper highlight several key results: the inclusion of an exemplar task pool improved average task success rates by 21%, while the verification model outperformed baselines by increasing success rates for novel tasks by 24% and encountered tasks by 36%. These findings underscore the effectiveness of the verification model in enhancing generative skill acquisition.

The two primary innovations embodied in VERGSA include:

1. Seamless Extension and Task Contextualization: By leveraging a dynamic exemplar task pool, VERGSA bridges the gap between mathematical verification and embodied task reasoning. This extension supports the generation of subtask-level training supervision and enables seamless real-time task adaption.
2. Automated, Scalable Reward Labeling: Utilizing Monte Carlo Tree Search (MCTS), VERGSA develops a labelled Process Reward Model (PRM) facilitating automatic supervision synthesis. This approach allows for efficient evaluation and refinement of skill sequences drawn from a wide pool of novel and contextually similar tasks.

VERGSA's methodical approach offers significant implications for both theoretical and practical advancement. Theoretically, it provides a structured mechanism for validating the reasoning processes involved in generative skill acquisition, potentially serving as a foundational scaffold for further research in the domain. Practically, it reduces the burden on computational resources by optimizing task verification through an automated scalable reward-paying process, hence aligning subtask success metrics closely with overall task goal achievement.

Nevertheless, some limitations persist. The computational efficiency of learning complex skills remains hampered by the requirement for comprehensive policy training for each distinct subtask, necessitating further scaling capability to accommodate real-world variability and sensorimotor noise—issues often encountered when transitioning from simulation to real-world applications. Future research could explore advancements in meta-learning and hierarchical reinforcement learning to identify transferable subskills across tasks, thus minimizing redundant policy training efforts. Addressing the sim2real gap, which curtails direct applicability in real-world robotics, will also be crucial; domain randomization and refined physical modeling present promising avenues for future exploration.

This paper marks a significant contribution to the intersection of verification models and embodied reasoning for AI skill acquisition, establishing a robust groundwork for real-time verification-driven learning paradigms.