A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects

• URL: http://arxiv.org/abs/2502.13964v1
• Date: Wed, 19 Feb 2025 18:59:17 GMT
• Title: A Training-Free Framework for Precise Mobile Manipulation of Small Everyday Objects
• Authors: Arjun Gupta, Rishik Sathua, Saurabh Gupta,
• Abstract summary: We develop a closed-loop training-free framework that enables a mobile manipulator to tackle precise tasks involving the manipulation of small objects. SVM employs an RGB-D wrist camera and uses visual servoing for control. We demonstrate that open-vocabulary object detectors can serve as a drop-in module to identify semantic targets.
• Score: 16.018172627950857
• License:
• Abstract: Many everyday mobile manipulation tasks require precise interaction with small objects, such as grasping a knob to open a cabinet or pressing a light switch. In this paper, we develop Servoing with Vision Models (SVM), a closed-loop training-free framework that enables a mobile manipulator to tackle such precise tasks involving the manipulation of small objects. SVM employs an RGB-D wrist camera and uses visual servoing for control. Our novelty lies in the use of state-of-the-art vision models to reliably compute 3D targets from the wrist image for diverse tasks and under occlusion due to the end-effector. To mitigate occlusion artifacts, we employ vision models to out-paint the end-effector thereby significantly enhancing target localization. We demonstrate that aided by out-painting methods, open-vocabulary object detectors can serve as a drop-in module to identify semantic targets (e.g. knobs) and point tracking methods can reliably track interaction sites indicated by user clicks. This training-free method obtains an 85% zero-shot success rate on manipulating unseen objects in novel environments in the real world, outperforming an open-loop control method and an imitation learning baseline trained on 1000+ demonstrations by an absolute success rate of 50%.

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