Personalized Robotic Object Rearrangement from Scene Context (2505.11108v2)

Abstract: Object rearrangement is a key task for household robots requiring personalization without explicit instructions, meaningful object placement in environments occupied with objects, and generalization to unseen objects and new environments. To facilitate research addressing these challenges, we introduce PARSEC, an object rearrangement benchmark for learning user organizational preferences from observed scene context to place objects in a partially arranged environment. PARSEC is built upon a novel dataset of 110K rearrangement examples crowdsourced from 72 users, featuring 93 object categories and 15 environments. To better align with real-world organizational habits, we propose ContextSortLM, an LLM-based personalized rearrangement model that handles flexible user preferences by explicitly accounting for objects with multiple valid placement locations when placing items in partially arranged environments. We evaluate ContextSortLM and existing personalized rearrangement approaches on the PARSEC benchmark and complement these findings with a crowdsourced evaluation of 108 online raters ranking model predictions based on alignment with user preferences. Our results indicate that personalized rearrangement models leveraging multiple scene context sources perform better than models relying on a single context source. Moreover, ContextSortLM outperforms other models in placing objects to replicate the target user's arrangement and ranks among the top two in all three environment categories, as rated by online evaluators. Importantly, our evaluation highlights challenges associated with modeling environment semantics across different environment categories and provides recommendations for future work.

An Overview of PARSEC: Preference Adaptation for Robotic Object Rearrangement from Scene Context

The paper "PARSEC: Preference Adaptation for Robotic Object Rearrangement from Scene Context" proposes an innovative benchmark and dataset aimed at addressing personalization challenges in robotic object rearrangement tasks within household environments. The authors introduce PARSEC to facilitate research into learning organizational preferences based solely on environmental context, supporting meaningful object placement even when dealing with unseen objects and new environments.

Key Contributions

PARSEC Benchmark and Dataset: The PARSEC benchmark is structured around a dataset comprised of 110,000 rearrangement examples sourced from 72 users, encompassing 93 object categories in 15 diverse environments. This dataset was meticulously crowdsourced to capture real user organizational preferences, which include variations within similar environments that are critical for training models to predict how robots should rearrange objects according to different user needs.

ContextSortLM Model: The authors also put forth ContextSortLM, a model leveraging LLMs specifically tailored for personalized object rearrangement. It functions by integrating context derived from both prior observations and the current scene to adaptively place objects. ContextSortLM stands out by outperforming existing models when tasked with replicating a target user’s arrangement preferences across various environments.

Evaluation and Findings

The paper thoroughly evaluates ContextSortLM against other personalized rearrangement models using the PARSEC benchmark. The results indicate that models utilizing multiple semantic context sources substantially outperform those relying on singular sources. Notably, ContextSortLM ranks among the top two models in all three environment categories, as determined by online evaluators. This suggests that leveraging diverse context sources can enhance object placement strategies by better aligning with user preferences.

The paper also underscores the inherent challenges in modeling environment semantics, particularly across different categories, which can result in discrepancies in inferred user preferences. However, integrating comprehensive scene context solutions as proposed can alleviate some of these difficulties by providing a more holistic understanding of user preferences within cluttered environments.

Practical and Theoretical Implications

The research highlighted in this paper is pivotal for advancing autonomous systems engaged in household chores and assistance. The ability to adapt to user preferences without explicit instruction is vital for improving robots’ usability and acceptance in daily life. On a theoretical level, the work explores integrating advanced LLM methodologies with task-specific automated systems, paving the way for more adaptable AI applications that can efficiently learn from context without human intervention.

Future Directions

The research community might consider exploring hybrid models that combine LLM-based reasoning with specialized policies, particularly in scenarios involving densely packed environments where ContextSortLM shows limitations. Additionally, improvements in encoding spatial and utility-based semantic information about environment surfaces could lead to enhancements in adaptive object rearrangement, thereby further aligning model outputs with complex user preferences.

In conclusion, "PARSEC: Preference Adaptation for Robotic Object Rearrangement from Scene Context" makes significant strides towards personalized robotic assistance, providing a robust benchmark for continued innovation in the domain of autonomous cooperative robotic systems.