Organizing objects by predicting user preferences through collaborative filtering

Abdo, Nichola; Stachniss, Cyrill; Spinello, Luciano; Burgard, Wolfram

doi:10.1177/0278364916649248

Cited by 8 publications

(6 citation statements)

References 51 publications

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“…First, relative object placement instructions do not allow for fine-grained target specification due to its inherent ambiguity. Addressing this issue would require learning user preferences from feedback [25]. Second, we observe some failure cases for large object placements, because of missing 3D priors of the objects to be placed.…”

Section: Conclusion and Discussionmentioning

confidence: 98%

Composing Pick-and-Place Tasks by Grounding Language

Mees

Burgard

2021

Experimental Robotics

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Controlling robots to perform tasks via natural language is one of the most challenging topics in human-robot interaction. In this work, we present a robot system that follows unconstrained language instructions to pick and place arbitrary objects and effectively resolves ambiguities through dialogues. Our approach infers objects and their relationships from input images and language expressions and can place objects in accordance with the spatial relations expressed by the user. Unlike previous approaches, we consider grounding not only for the picking but also for the placement of everyday objects from language. Specifically, by grounding objects and their spatial relations, we allow specification of complex placement instructions, e.g. "place it behind the middle red bowl". Our results obtained using a real-world PR2 robot demonstrate the effectiveness of our method in understanding pick-and-place language instructions and sequentially composing them to solve tabletop manipulation tasks. Videos are available at http://speechrobot.cs.unifreiburg.de.

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Section: Conclusion and Discussionmentioning

confidence: 98%

Composing Pick-and-Place Tasks by Grounding Language

Mees

Burgard

2021

Experimental Robotics

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“…For example, ref. [26] addressed personal preferences by combining a relation model for learning preferences for arranging objects on a shelf with object detection. However, while their approach could even successfully handle conflicting preferences, it also missed subtle differences between spatial relations in terms of their extent.…”

Section: Scene Recognitionmentioning

confidence: 99%

Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots

Meißner,

Dillmann

2023

Robotics

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This article describes an approach for mobile robots to identify scenes in configurations of objects spread across dense environments. This identification is enabled by intertwining the robotic object search and the scene recognition on already detected objects. We proposed “Implicit Shape Model (ISM) trees” as a scene model to solve these two tasks together. This article presents novel algorithms for ISM trees to recognize scenes and predict object poses. For us, scenes are sets of objects, some of which are interrelated by 3D spatial relations. Yet, many false positives may occur when using single ISMs to recognize scenes. We developed ISM trees, which is a hierarchical model of multiple interconnected ISMs, to remedy this. In this article, we contribute a recognition algorithm that allows the use of these trees for recognizing scenes. ISM trees should be generated from human demonstrations of object configurations. Since a suitable algorithm was unavailable, we created an algorithm for generating ISM trees. In previous work, we integrated the object search and scene recognition into an active vision approach that we called “Active Scene Recognition”. An efficient algorithm was unavailable to make their integration using predicted object poses effective. Physical experiments in this article show that the new algorithm we have contributed overcomes this problem.

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“…While each user can have a different preference, our goal is to cluster the users to a small set of dominant preferences. Such groups of similar users, also called personas, can be built manually from questionnaires [33] or through collaborative filtering [34], [35]. Other related work includes identifying different driver styles [36]- [38] using features from vehicle trajectories, human motion prototypes [39] for robot navigation and human preference stereotypes for human-robot interaction [40].…”

Section: Related Workmentioning

confidence: 99%

Two-Stage Clustering of Human Preferences for Action Prediction in Assembly Tasks

Nemlekar¹,

Modi²,

Gupta³

et al. 2021

Preprint

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To effectively assist human workers in assembly tasks a robot must proactively offer support by inferring their preferences in sequencing the task actions. Previous work has focused on learning the dominant preferences of human workers for simple tasks largely based on their intended goal. However, people may have preferences at different resolutions: they may share the same high-level preference for the order of the sub-tasks but differ in the sequence of individual actions. We propose a two-stage approach for learning and inferring the preferences of human operators based on the sequence of sub-tasks and actions. We conduct an IKEA assembly study and demonstrate how our approach is able to learn the dominant preferences in a complex task. We show that our approach improves the prediction of human actions through cross-validation. Lastly we show that our two-stage approach improves the efficiency of task execution in an online experiment and demonstrate its applicability in a real-world robot-assisted IKEA assembly.

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Organizing objects by predicting user preferences through collaborative filtering

Cited by 8 publications

References 51 publications

Composing Pick-and-Place Tasks by Grounding Language

Composing Pick-and-Place Tasks by Grounding Language

Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots

Two-Stage Clustering of Human Preferences for Action Prediction in Assembly Tasks

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