ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenes

TL;DR

ConSOR is a framework that enables robots to rearrange objects in partially organized scenes by leveraging contextual cues, eliminating the need for explicit goal specifications from users, and demonstrating superior generalization capabilities.

Contribution

The paper introduces ConSOR, a novel context-aware framework that uses scene context to guide object rearrangement without explicit user-defined goals.

Findings

ConSOR outperforms baseline methods in generalizing to new arrangements.

It effectively handles unseen object categories.

The approach reduces user burden in complex environments.

Abstract

Object rearrangement is the problem of enabling a robot to identify the correct object placement in a complex environment. Prior work on object rearrangement has explored a diverse set of techniques for following user instructions to achieve some desired goal state. Logical predicates, images of the goal scene, and natural language descriptions have all been used to instruct a robot in how to arrange objects. In this work, we argue that burdening the user with specifying goal scenes is not necessary in partially-arranged environments, such as common household settings. Instead, we show that contextual cues from partially arranged scenes (i.e., the placement of some number of pre-arranged objects in the environment) provide sufficient context to enable robots to perform object rearrangement \textit{without any explicit user goal specification}. We introduce ConSOR, a Context-aware Semantic Object Rearrangement framework that utilizes contextual cues from a partially arranged initial state of the environment to complete the arrangement of new objects, without explicit goal specification from the user. We demonstrate that ConSOR strongly outperforms two baselines in generalizing to novel object arrangements and unseen object categories. The code and data can be found at https://github.com/kartikvrama/consor.