# Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online

## TL;DR

This paper presents a history-aware verifier that improves online interaction decisions by reasoning about past interactions, significantly enhancing performance in ambiguous scenarios involving robots and complex objects.

## Contribution

The paper introduces a novel history-aware verifier that explicitly decouples action generation from verification, improving decision-making in ambiguous online interactions.

## Key findings

- Empirical results show improved interaction success rates across multiple environments.
- Theoretically, employing a verifier enhances expected action quality.
- Demonstrates effectiveness over baseline methods in real-world and simulated tests.

## Abstract

We introduce a novel History-Aware VErifier (HAVE) to disambiguate uncertain scenarios online by leveraging past interactions. Robots frequently encounter visually ambiguous objects whose manipulation outcomes remain uncertain until physically interacted with. While generative models alone could theoretically adapt to such ambiguity, in practice they obtain suboptimal performance in ambiguous cases, even when conditioned on action history. To address this, we propose explicitly decoupling action generation from verification: we use an unconditional diffusion-based generator to propose multiple candidate actions and employ our history-aware verifier to select the most promising action by reasoning about past interactions. Through theoretical analysis, we demonstrate that employing a verifier significantly improves expected action quality. Empirical evaluations and analysis across multiple simulated and real-world environments including articulated objects, multi-modal doors, and uneven object pick-up confirm the effectiveness of our method and improvements over baselines. Our project website is available at: https://liy1shu.github.io/HAVE_CoRL25/