Original: My House, My Rules: Learning Tidying Preferences with Graph Neural Networks
Revised: My House, My Rules: Learning Tidying Preferences with Graph Neural Networks

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AfﬁliationAddress email
Revised: AfﬁliationAddress email

Original: Abstract: Robots that arrange household objects should do so according to theuser’s preferences, which are inherently subjective and difﬁcult to model.
Revised: Abstract: Robots that arrange household objects should do so according to theuser’s preferences, which are inherently subjective and difﬁcult to model.

Original: Wepresent NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vectorfrom a user by observing how they arrange scenes.
Revised: Wepresent NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vectorfrom a user by observing how they arrange scenes.

Original: Given any set of objects, thisvector can then be used to generate an arrangement which is tailored to that user’sspatial preferences, with word embeddings used for generalization to new objects.
Revised: Given any set of objects, thisvector can then be used to generate an arrangement which is tailored to that user’sspatial preferences, with word embeddings used for generalisation to new objects.

Original: We develop a tidying simulator to gather rearrangement examples from 75 users,and demonstrate empirically that our method consistently produces neat and personalized arrangements across a variety of rearrangement scenarios.
Revised: We develop a tidying simulator to gather rearrangement examples from 75 users,and demonstrate empirically that our method consistently produces neat and personalised arrangements across a variety of rearrangement scenarios.

Original:  Introduction
Revised:  Introduction

Original: Rearranging objects in unstructured environments is a fundamental task in robotics.
Revised: Rearranging objects in unstructured environments is a fundamental task in robotics.

Original: For example,a domestic robot could be required to set a dinner table, tidy a messy desk, and ﬁnd a home for anewly-bought object.
Revised: For example,a domestic robot could be required to set a dinner table, tidy a messy desk, and ﬁnd a home for anewly-bought object.

Original: Given goal states stipulating where each object should go, many approachesexist for planning a sequence of actions to manipulate objects into the desired arrangement [1].
Revised: Given goal states stipulating where each object should go, many approachesexist for planning a sequence of actions to manipulate objects into the desired arrangement [1, 2, 3].

Original: But how does a robot know what these goal states should be, for a rearrangement to be consideredsuccessful, tidy, and aesthetically pleasing?
Revised: But how does a robot know what these goal states should be, for a rearrangement to be consideredsuccessful, tidy, and aesthetically pleasing?

Original: The ability of a robot to understand human preferences,especially regarding the handling of personal objects, is a key priority for users [2].
Revised: The ability of a robot to understand human preferences,especially regarding the handling of personal objects, is a key priority for users [4].

Original: Several factorsinvolved in spatial preferences are universal: lining up pencils on a desk in parallel is more aesthetically pleasing than scattering them randomly, and stacking plates away in a cupboard rather than onthe sofa is common sense.
Revised: Several factorsinvolved in spatial preferences are shared across many users: symmetry and usefulness are preferableto chaos. Stacking plates neatly inside a cupboard rather than scattering them randomly across a sofais common sense.

Original: However, many of the factors involved are inherently personal, as is theirrelative prioritization.
Revised: However, many of the factors involved are inherently personal, as is their relativeprioritisation.

Original: For example, is the person left or right-handed?
Revised: For example, is the person left or right-handed?

Original: Do they want their favoritebook tidied away neatly on a shelf, or placed on the table nearby for convenience?
Revised: Do they want their favourite booktidied away neatly on a shelf, or placed on the table nearby for convenience?

Original: How high is theirrisk tolerance — do they deliberately keep fragile glasses further from the edge of the shelf, even ifit makes them harder to reach?
Revised: How high is their risktolerance — do they deliberately keep fragile glasses further from the edge of the shelf, even if itmakes them harder to reach?

Original: Do they order food in the fridge with the tallest objects at the backand the shortest at the front?
Revised: Do they order food in the fridge with the tallest objects at the backand the shortest at the front?

Original: Or do they keep the most frequently used items at the front and easilyreachable?
Revised: Or do they keep the most frequently used items at the front and easilyreachable?

Original: In this case, how does the robot know which items are most frequently used and oughtto be placed near the front?
Revised: In this case, how does the robot know which items are most frequently used and oughtto be placed near the front?

Original: This in itself depends on personal preference.
Revised: This in itself depends on personal preference.

Original: It is clear that spatial preferences are a complex, multi-faceted, and subjective concept.
Revised: It is clear that spatial preferences are a complex, multi-faceted, and subjective concept.

Original: In this paper,we propose an object rearrangement strategy which models these preferences with a latent user vector representation, inferred by directly observing how a user organizes their home environment.
Revised: In this paper,we propose an object rearrangement strategy which models these preferences with a latent user vector representation, inferred by directly observing how a user organises their home environment.

Original: Thenetwork is trained on arrangements made by a set of training users.
Revised: Thenetwork is trained on arrangements made by a set of training users.

Original: For a test user, the robot will observe how they have arranged their home.
Revised: For a test user, the robot will observe how they have arranged their home.

Original: This can be achieved by a robot using existing techniquesfor localization, object identiﬁcation and pose estimation [3, 4, 5].
Revised: This can be achieved by a robot using existing techniquesfor localisation, object identiﬁcation and pose estimation [5, 6, 7].

Original: By passing these example scenesthrough the Variational Autoencoder, the network will extract this test user’s preference vector.
Revised: By passing these example scenesthrough the Variational Autoencoder, the network will extract this test user’s preference vector.

Original: Thiscan then be used by a robot to tidy up these scenes, and also predict personalized arrangements fornew objects and new scenes based on how training users with “similar” preferences to this test userarranged them.
Revised: Thiscan then be used by a robot to tidy up these scenes, and also predict personalised arrangements fornew objects and new scenes based on how training users with “similar” preferences to this test userarranged them.

Original: These predicted arrangements can then be used as target states for planning andmanipulation algorithms [1].
Revised: These predicted arrangements can then be used as target states for planning andmanipulation algorithms [1].

Original: [Figure]
Revised: [Figure]

Original: We make the following contributions, adding a layer of personalisation to 
Revised: We make the following contributions, adding a layer of personalisation to robotic rearrangement

Original: Word Embeddings for Object Generalization .
Revised: Word Embeddings for Object Generalisation .

Original: Our network extracts semantic features for an object from the word embedding of its name, enabling generalization to objects unseen during training.
Revised: Our network extracts semantic features for an object from the word embedding of its name, enabling generalisation to objects unseen during training.

Original: User-Driven Evaluation .
Revised: User-Driven Evaluation .

Original: We develop TidySim: a 3D simulator web app for users to arrange scenes.
Revised: We develop TidySim: a 3D simulator web app for users to arrange scenes.

Original: With rearrangement data from 75 users, we demonstrate empirically that our method can learn spatial preferences, and predict personalized arrangements which users rate highly.
Revised: With rearrangement data from 75 users, we demonstrate empirically that our method can learn spatial preferences, and predict personalised arrangements which users rate highly.

Original: Visualizations ofpredicted arrangements are available in our supplementary material (along with our code) and onour anonymous project page: sites.google.com/view/tidying-preferences.
Revised: Visualisations ofpredicted arrangements are available in our supplementary material (along with our code onour anonymous project sites

Original:  Related Work
Revised:  Related Work

Revised: A crucial challenge in the ﬁeld of robotic rearrangement is predicting a position for each object inorder to form a tidy arrangement.

Revised: Prior approaches discretise the problem by semantically groupingobjects into containers [8, 9], learn Dirichlet Process models of common object locations [10, 11], oroptimise a cost function based on one user’s examples [12].

Revised: We now discuss these three approaches,and then brieﬂy reference existing neural methods for learning non-spatial user preferences.

Original: Grouping semantically related objects.
Revised: Grouping semantically related objects.

Original: Collaborative ﬁltering [6] can be used to group objects byshelf. It predicts how a user would categorize a given object based on how similar users categorizedit.
Revised: Collaborative ﬁltering [8] can predict how a user wouldcategorise an object based on how similar users categorised it.

Original: Data is mined fromonline object taxonomies such as shopping websites to generalize to objectsnot seen during training: preferences about known objects nearby in the hierarchy are used to predicthow to tidy a  new object.
Revised: Data is mined from object taxonomiessuch as shopping websites to generalise to new objects: preferences about known objects nearby inthe hierarchy are used to select a shelf for the new object.

Original: Object taxonomies can also be used to predict the correct cupboard inwhich to place an object, viewed as a classiﬁcation task [7].
Revised: Object taxonomies can also be used toplace an object into the correct cupboard, viewed as a classiﬁcation task [9].

Original: While these approaches are effectivein grouping objects into boxes or cupboards, our method addresses the general case of learningcontinuous spatial relations, like how to set a dining table or tidy an ofﬁce desk.
Revised: While these approachesare effective in grouping objects into boxes or cupboards, our method addresses the general case oflearning continuous spatial relations, like how to set a dining table or tidy an ofﬁce desk.

Original: Dirichlet Process models .
Revised: Dirichlet Process models .

Original: Recent work [8] learns spatial concepts for tidying up: that is, therelationship between an object, its tidied location, and a name to describe that location, so that voicecommands can be used.
Revised: Recent work [10] learns spatial concepts : i.e. the relationship betweenan object, its tidied location, and a name to describe that location (useful for voice commands).

Original:  Gibbs Sampling is used to learn model parameters.
Revised: Model parameters are learned with Gibbs Sampling.

Original: Another approachwith Dirichlet Process modeling uses context from sampled human poses [9] to place objects inconvenient locations.
Revised: Another approach with Dirichlet Process modelling uses context from sampled human poses [11] to position objects conveniently.

Original: Density function parameters are shared across objects of the same type.
Revised: Density function parameters are shared across objects of the same type.

Original: Thisapproach is evaluated on a training dataset of arrangements made by 3-5 users, with a further 2 usersscoring arrangements.
Revised: This approach is evaluated on a trainingdataset of arrangements made by 3-5 users, with a further 2 users scoring arrangements.

Original: While these methods predict generally tidy arrangements, it is not shownwhether they can tailor them to individual preferences.
Revised: While thesemethods predict generally tidy arrangements, it is not shown whether they can tailor them to individual preferences.

Original: We develop a tidying simulator to gatherdata on a larger scale, demonstrating that our neural encoder makes personalized predictions on adataset of 75 users.
Revised: We develop a tidying simulator to gather data on a larger scale, demonstratingthat our neural encoder makes personalised predictions on a dataset of 75 users.

Original: Furthermore, our method uses word embeddings to predict positions for objectsunseen during training, and learns transferable representations of tidying preferences which can beapplied to arrange new scenes.
Revised: Furthermore, ourmethod uses word embeddings to predict positions for objects unseen during training, and learnstransferable representations of tidying preferences which can be applied to arrange new scenes.

Original: Example-driven tidiness loss function.
Revised: Example-driven tidiness loss function.

Original: Another approach is to ask each user to provide several tidyarrangements for a scene, to serve as positive examples [10].
Revised: Another approach is to ask each user to provide several tidyarrangements for a scene, to serve as positive examples [12].

Original: To tidy a scene, the robot computes atarget tidy layout using a cost function which encourages altering the object-object distances in theuntidy scene to match those in the “closest” tidy positive example.
Revised: To tidy a scene, the robot computes atarget tidy layout using a cost function which encourages altering the object-object distances in theuntidy scene to match those in the “closest” tidy positive example.

Original: Simulated re-annealing is used tooptimize this cost function efﬁciently.
Revised: Simulated re-annealing is usedto optimise this cost function efﬁciently.

Original: We add value ﬁrstly because our method generalizes to newobjects without requiring users to arrange them manually, and secondly because our method applies prior knowledge from other users, meaning that it works well even when the positions of objects inan individual positive example are noisy, as in real-world use cases.
Revised: We add value ﬁrstly because our method generalises tounseen objects without requiring users to arrange them manually, and secondly because our method combines knowledge about the current user’s preferences with prior knowledge about “similar”training users, correcting errors in individual examples and allowing for stronger generalisation.

Original: Neural recommender systems.
Revised: Neural recommender systems.

Original: Neural networks have been used for making recommendationsbased on user preferences [11].
Revised: Neural networks have been used for making recommendationsbased on user preferences [13].

Original: The YouTube neural recommender system [12] predicts a user’sexpected watch time for a future video based on past videos watched and searches made.
Revised: The YouTube neural recommender system [14] predicts a user’sexpected watch time for a future video based on past videos watched and searches made.

Original: However,these methods do not address the challenges of learning spatial relations by observing scenes andpredicting tidy arrangements for any set of objects.
Revised: However,these methods do not address the challenges of learning spatial relations by observing scenes andpredicting tidy arrangements for any set of objects.

Original: We apply GNN components to solve this.
Revised: We apply GNN components to solve this.

Original:  Method
Revised:  Method

Original: Here, we introduce NeatNet: a novel architecture for learning spatial preferences.
Revised: We introduce NeatNet: a novel architecture for learning spatial preferences.

Original: First, we describe a Variational Autoencoder for learning latent representations of a user’s preferences (Section3.1).
Revised: First, we describe a Variational Autoencoder for learning latent representations of a user’s preferences (Section 3.1).

Original: Then, we introduce the GNN layers, which allow NeatNet to learn from scenes which havebeen tidied by users, modeled as graphs of objects (Section 3.2).
Revised: Then we introduce the GNN layers which allow NeatNet to learn from scenes arranged by users,modelled as graphs of objects (Section 3.2).

Original: Next, we adapt the architectureto enable learning from multiple example scenes arranged by one user (Section 3.3).
Revised: Next, we adapt the architecture to learn from multipleexample scenes per user (Section 3.3).

Original: Finally, weprovide details on how object semantic embeddings are generated from word embeddings, allowinggeneralization to unseen objects (Section 3.4).
Revised: Finally, we illustrate how object semantic embeddings areinferred from word embeddings, for generalisation to unseen objects (Section 3.4).

Original: 3.1 Encoding User Preferences with VAEs
Revised: 3.1 Encoding User Preferences with VAEs

Original: Our objective is to learn a user encoder function E φ , represented by a neural network with learnedparameters φ .
Revised: Our objective is to learn a user encoder function E φ , represented by a neural network with learnedparameters φ .

Original: Its output is a user’s preference vector u , and its input is a representation of a scenearrangement made by that user.
Revised: Its output is a user’s preference vector u . Its input is a representation of a scenearrangement made by that user.

Original: Each scene is deﬁned by the terms s (the semantic identities of theobjects in the scene, further explained in Section 3.4), and p (the position encodings for the objectsin the scene, e.g. coordinates).
Revised: Each scene is deﬁned by the terms s (the semantic identities ofthe objects in the scene, further explained in Section 3.4), and p (the position encodings for thoseobjects, e.g. coordinates).

Original: These are not passed into the VAE directly: scenes are ﬁrst encodedusing GNN layers (Section 3.2), so that the network can operate on scenes with a variable numberof objects.
Revised: These are not passed into the VAE directly: scenes are ﬁrst encodedusing GNN layers (Section 3.2), since scenes can have a variable number of objects.

Original: We also train a position predictor function D θ .
Revised: We also train aposition predictor function D θ .

Original: Given a set of objects identiﬁed by s ,and a user preference vector u , this predicts a position for each object which is consistent with thosespatial
Revised: Given a set of objects identiﬁed by s , and a user preference vectoru , this predicts a position for each object which reﬂects those spatial preferences.

Original: [Figure]
Revised: [Figure]

Revised: points in the user latent space based on how they arrange a dining scene.

Revised: Users close together in this latent vector space share similar spatial preferences.

Revised: These networks are trained end-to-end as a Variational Autoencoder [15].

Revised: A scene represented by sand p is passed into the encoder E φ .

Revised: The output user preference vector u , along with the semanticembeddings of the objects s , is passed into the position predictor D θ , which acts as a decoder byreconstructing the original scene.

Revised: Since the user preference vector is a low-dimensional bottleneck,this encourages the network to learn important spatial preferences like left/right-handedness whichare useful for predicting the positions of many objects at once.

Revised: For variational inference, the encoder

Revised: E φ predicts a Gaussian distribution over the possible user vectors u which could explain the inputscene.

Revised: At training time, we sample from this distribution to obtain u , and we take the mean atinference time.

Revised: The VAE objective is thus φ ∗ , θ ∗ (cid:44) argmin φ,θ L ( φ, θ ) .

Original: [Equation]
Revised: [Equation]

Original: Here, p data represents the training data distribution.
Revised: Here, p data represents the training data distribution.

Original: p ( u ) is a prior over the user preference vectors:a standard Gaussian, with zero mean and unit variance.
Revised: p ( u ) is a prior over the user preference vectors: a standard Gaussian, with zero mean and unit variance.

Original: KL represents the Kullback–Leibler divergence (standard in VAEs [13]) which encourages the estimated u distributions to stay close to thep ( u ) Gaussian prior over the user space, and the hyperparameter β scales this prior term to make thelatent space less sparse and encourage disentanglement [14].
Revised: KL represents the Kullback–Leiblerdivergence (standard in VAEs [15]) which encourages the estimated u distributions to stay close to the p ( u ) Gaussian prior over the user space, and the hyperparameter β scales this prior term to makethe latent space less sparse and encourage disentanglement [16].

Original: 3.2 Encoding Scenes with GNN Layers
Revised: 3.2 Encoding Scenes with GNN Layers

Original: We will now encode scenes as vectors so that they can be passed through the VAE.
Revised: We will now encode scenes as vectors so that they can be passed through the VAE.

Original: To do this, weuse Graph Neural Network (GNN) layers.
Revised: To do this, weuse Graph Neural Network (GNN) layers.

Original: GNNs can operate on scenes with a variable number ofarbitrary nodes, which is critical since the robot cannot foresee all the objects it will ever encounter.
Revised: GNNs can operate on scenes with a variable number ofarbitrary nodes, which is critical since the robot cannot foresee all the objects it will ever encounter.

Original: Each node represents an object in the scene.
Revised: Each node represents an object in the scene.

Original: Node i has feature vector x
Revised: Node i has feature vector x

Original: i formed by concatenatingthe semantic embedding for that object s i with its position encoding p i .
Revised: i formed by concatenatingthe semantic embedding for that object s i with its position encoding p i .

Original: The scene graph is fullyconnected, to avoid making assumptions about which object-object relations are relevant or not.
Revised: The scene graph is fullyconnected, to avoid making assumptions about which object-object relations are relevant or not.

Original: [Figure]
Revised: [Figure]

Original: layers separated by LeakyReLU activations [16].
Revised: [Figure]

Original: The output is a mean and variance for the user preference vector distribution, from which we sample to obtain u . We illustrate some examples of user preferences that might be captured in u (e.g. left-handedness), though of course this is  a latent vector learned with an unsupervised method.
Revised: Our experiments focus on positions, but the position vector can be extended to include orientation information. Similarly to the graph encoder, the position predictor consists of a sequence of GAT layers, after which each node is passed through a series of fully-connected layers, the ﬁnal output of which is the predicted position vector for that node.

Original: [Figure] Both the graph encoder (Figure 3) and position predictor (Figure 4) use GNN layers.
Revised: Both the graph encoder (Figure 3) and position predictor (Figure 4) use GNN layers.

Original: As is standardfor GNN layers, we need to compute the hidden feature vector h i for each node i , based on itscurrent feature vector
Revised: As is standardfor GNN layers, we need to compute the hidden feature vector h i for each node i , based on itscurrent feature vector

Original: x i as well as the node feature vectors x j of its neighbours.
Revised: x i as well as the node feature vectors x j of its neighbours.

Original: To compute h i ,we choose the Graph Attention (GAT) layer [17].
Revised: To compute h i , wechoose the Graph Attention (GAT) layer [19].

Original: h i is computed as a weighted sum of the nodefeature vectors x j in the current node’s neighbourhood, where the weights α ij are generated by thesoft attention mechanism.
Revised: h i is computed as a weighted sum of the node featurevectors x j in the current node’s neighbourhood, where the weights α ij are generated by the soft attention mechanism.

Original: Since our graph is fully connected, we calculate attention weights for allthe other objects in the scene: therefore the attention mechanism allows the network to choose foritself which object-to-object relations are the most important for predicting correct arrangements.
Revised: Since our graph is fully connected, we calculate attention weights for all theother objects in the scene: therefore the attention mechanism allows the network to autonomouslydecide which object-to-object relations are the most important for predicting correct arrangements.

Original: In order to further increase the power of the graph network, we can add a second GAT layer whichtakes as input the h i features produced by the ﬁrst layer.
Revised: In order to further increase the power of the graph network, we can add a second GAT layer whichtakes as input the h i features produced by the ﬁrst layer.

Original: The computation of h iby the GAT layersproceeds as follows, where (cid:107) represents concatenation, and the learned parameters are the weights W and a :
Revised: A GAT layer computes h i as follows, where(cid:107) represents concatenation, and the learned parameters are the weights W and a :

Original: [Equation]
Revised: [Equation]

Original: 3.3 Generalizing Across Scenes
Revised: 3.3 Generalising Across Scenes

Original: Many spatial preferences, such as left/right-handedness, will be exhibited by the user consistentlyacross scenes.
Revised: Many spatial preferences, such as left/right-handedness, will be exhibited by the user consistentlyacross scenes.

Original: We now extend NeatNet to learn from multiple scenes arranged by the same user, asshown in Figure 5.
Revised: We now extend NeatNet to learn from multiple scenes arranged by the same user, asshown in Figure 5.

Original: Every example scene arranged by the current user is passed through the singlescene encoder network E φ in Figure 3.
Revised: Every example scene arranged by the current user is passed through the singlescene encoder network E φ in Figure 3.

Original: Each scene may produce a subtly different estimate of theuser’s preferences, so they are aggregated through a global pooling layer to produce an average userpreference vector u .
Revised: Each scene may produce a subtly different estimate of theuser’s preferences, so they are aggregated through a global pooling layer to produce an average userpreference vector u .

Original: On the decoding side, we use the same u vector as the input to the positionpredictor to reconstruct all the scenes created by this user.
Revised: On the decoding side, we use the same u vector as the input to the positionpredictor to reconstruct all the scenes created by this user.

Original: Since u is the architecture’s bottleneck,this encourages the network to learn important preference features which generalize across scenes,such as left/right handedness. [Figure]
Revised: Since u is the architecture’s bottleneck,this encourages the network to learn important preference features which generalise across scenes,such as left/right handedness or a preference for arranging objects compactly rather than spaciously.

Original: [Figure]
Revised: [Figure]

Revised: 3.4 Semantic Embeddings

Original: Each object’s identity is represented by its semantic embedding vector.
Revised: Each object’s identity is represented by its semantic embedding vector.

Original: A straightforward approachis to use one-hot encoding.
Revised: One-hot encoding is astraightforward approach.

Original: A limitation of this method is that it does not generalize readily to newobjects.
Revised: A limitation of this method is that it does not generalise readily to newobjects.

Original: An alternative is feature vectors: example entries include the height of an object, or RGBvalues describing its color palette.
Revised: An alternative is feature vectors: example entries include the height of an object, or RGBvalues describing its colour palette.

Original: However, this may not capture all features necessary for tidying.
Revised: However, this may not capture all features necessary for tidying.

Original: [Figure]

Original: We also develop a method using word embeddings
Revised: We also develop a method using word embeddings.

Original: Objects which appear in similar linguisticcontexts are usedby people for similar activities, and so are often arranged in a related way: forexample “pen” and “pencil”, or “salt” and “pepper”.
Revised: Objects which appear in similar linguistic contexts are used for similar activities, and so are often arranged in a related way: for example “pen” and“pencil”, or “salt” and “pepper”.

Original: We load word embeddings from a FastTextmodel [18], pre-trained on the Common Crawl dataset of over 600 billion tokens.
Revised: We load word embeddings from a FastText model [20], pre-trainedon the Common Crawl dataset of over 600 billion tokens.

Revised: Names of objects are provided to NeatNeteither by the simulator or by an object detection system if deployed physically.

Original: Names of objectsare converted to word embeddings and passed through the semantic embedding extractor (Figure6), yielding the semantic embeddings referred to in Figure 3.
Revised: These names areconverted to word embeddings and passed through the semantic embedding extractor (Figure 6),yielding the semantic embeddings referred to in Figure 3.

Original: Thisapproach allowsour method topredict positions for new objects which the robot has never seen arranged during training.
Revised: This allows the robot to predict positionsfor new objects never seen before in any tidy arrangement during training.

Original: [Figure]  
Revised: [Figure]  Experiments

Original: 4.1 Collecting Rearrangement Data from Users
Revised: 4.1 Collecting Rearrangement Data from Users

Original: [Figure]

Original: Gathering data from human users at scale is a signiﬁcant challenge for robotics experiments.
Revised: Gathering data from human users at scale is a signiﬁcant challenge for robotics experiments.

Original: Wedeveloped TidySim: a 3D object rearrangement simulator deployed as a web app, allowing usersto arrange a series of scenes through an intuitive interface.
Revised: Wedeveloped TidySim: a 3D object rearrangement simulator deployed as a web app, allowing usersto arrange a series of scenes through an intuitive interface.

Original: Features include: moving the camera,clicking and dragging objects to move or rotate them, and an inventory for unplaced objects.[Figure]
Revised: Features include: moving the camera,clicking and dragging objects to move or rotate them, and an inventory for unplaced objects.

Original: .2 Experimental Procedure
Revised: 4.2 Experimental Procedure

Original: We designed the scenes in Figure 7 to maximise experimental value while keeping the user workloadlow ( ∼ 5 minutes total per user).
Revised: We designed the scenes in Figure 7 to maximise experimental value while keeping the user workloadlow ( ∼ 5 minutes total per user).

Original: Rearrangement data from 75 users was gathered by distributing theexperiment link on social media.
Revised: Rearrangement data from 75 users was gathered by distributing theexperiment link on social media.

Original: Each user submits a tidy arrangement for a number of scenes.
Revised: Each user submits a tidy arrangement for a number of scenes.

Original: Weset aside a group of 8 test users, male and female, and majority non-roboticists.
Revised: Weset aside a group of 8 test users, male and female, and majority non-roboticists.

Original: The training datasetwas formed from the 67 remaining users.
Revised: The training datasetwas formed from the 67 remaining users.

Original: NeatNet was trained by passing training users through the
Revised: NeatNet was trained by passing training users through the

Original: Graph VAE and updating parameters with a neural network optimizer according to the loss functionin Equation 3.1.
Revised: Graph VAE and updating parameters with a neural network optimiser according to the loss functionin Equation 3.1.

Original: For each test user, we passed their example scenes through NeatNet to extract theirspatial preferences, and predict a personalized arrangement for the test scene (varies by experiment).
Revised: For each test user, we passed their example scenes through NeatNet to extract theirspatial preferences, and predict a personalised arrangement for the test scene (varies by experiment).

Original: Arrangements were also produced by the baseline methods in Section 4.3.
Revised: Arrangements were also produced by the baseline methods in Section 4.3.

Original: The test user was thenasked to score the arrangements produced by each method based on how tidy they were accordingto their preferences, on a scale from 0 (completely untidy) to 10 (perfectly tidy).
Revised: The test user was thenasked to score the arrangements produced by each method based on how tidy they were accordingto their preferences, on a scale from 0 (completely untidy) to 10 (perfectly tidy).

Original: 4.3 Baselines
Revised: 4.3 Baselines

Original: In Section 2, we qualitatively compare with existing methods to show where we add new capabilities.
Revised: In Section 2, we qualitatively compare with existing methods to show where we add new capabilities.

Original: For a fair quantitative evaluation of our method on this dataset, we test against a varietyof baseline methods depending on the speciﬁc rearrangement experiment.
Revised: For a fair quantitative evaluation of our method on this dataset, we test against a variety of baseline methods depending on the speciﬁc rearrangement task.

Original: Positive-Example :to tidy a known scene, this baseline copies the example arrangement which was supplied bythe test user (which the NeatNet method uses to extract this user’s tidying preferences).
Revised: Positive-Example : to tidy a known scene, this baseline copies the example arrangement which was supplied by the test user (which the NeatNet method uses to extract this user’s tidying preferences).

Original: Thisbaseline ignores priors from other users and is unavailable when generalizing to new objectsor scenes.
Revised: This baseline ignores priors fromother users and is unavailable when generalising to new objects or scenes.

Original: Random-Position sets the missing object’s predicted position randomly, while Mean-Position uses the object ’s mean position across all the example arrangements from training users.
Revised: Random-Position setsthe missing object’s predicted position randomly, while Mean-Position uses the object’s meanposition across all the example arrangements from training users.

Original: NeatNet-No-Prefs is an ablation baseline: the test user’s preference vector is replacedwith the zero vector (representing a neutral preference, which test users might not ﬁnd tidy).
Revised: NeatNet-No-Prefs is an ablation baseline: the test user’s preference vector is replaced with the zero vector (representing aneutral preference, which test users might not ﬁnd tidy).

Original: The

Original:  Pose-Graph method produces an arrangement which some users will ﬁnd tidy, but is still not personalized to the current user’s preferences.
Revised: The Pose-Graph method produces anarrangement which some users will ﬁnd tidy, but is still not personalised to the current user’s preferences.

Original: It learns a probabilistic pose graph of relative objectpositions from the training example arrangements, and solves it using Monte Carlo sampling andscoring to ﬁnd the most likely arrangement.
Revised: It learns a probabilistic pose graph of relative object positions from the training examplearrangements, and solves it using Monte Carlo sampling and scoring to ﬁnd the most likely arrangement.

Original: When placing an unseen object, Mean-With-Offsetuses the mean position of the other objects in the scene, and adds a small random offset to minimisecollisions with other objects.
Revised: When placing an unseen object, Mean-With-Offset uses the mean position of theother objects in the scene, and adds a small random offset to minimise collisions with other objects.

Original: Nearest-Neighbor ﬁnds the object already placed by the user whichhas the closest word embedding vector to the unseen object, and places the new object next to it, i.e.it would place the salt next to the pepper.
Revised: Nearest-Neighbour ﬁnds the object already placed by the user which has the closest word embedding vector to the unseen object, and places the new object next to it, i.e. it would place thesalt next to the pepper.

Revised: Weighted-kNN-Regression uses the k = 3 nearest neighbours, weightedby word distance, to compute an average position.

Original: For the task of arranging a new scene, the Random-Userbaseline copies how a random training user arranged that scene, without personalisation.
Revised: For the task of arranging a new scene, the Random-User baseline copies how a random training user arranged that scene, without personalisation.

Revised: kNN-Scene-Projection projects the user’s preferences from the example scene onto thenew scene, by placing each object in the new scene based on how the user placed the k = 3 mostsimilar objects in the example scene, similar to the Weighted-kNN-Regression algorithm.

Original: 4.4 Experiment Results
Revised: 4.4 Experiment Results

Original: We designed a series of experiments to test our method’s capabilities.
Revised: We designed a series of experiments to test our method’s capabilities.

Original: Visualizations of predictedarrangements are available in our supplementary material and on our anonymous project page:sites.google.com/view/tidying-preferences.
Revised: Visualisations of predictedarrangements are available in our supplementary material and on our anonymous project page:sites.google.com/view/tidying-preferences.

Original: Can NeatNet Tidy a Known Scene?
Revised: Can NeatNet Tidy a Known Scene?

Original: (Table 1) Suppose that the robot has seen the user tidy a room.
Revised: (Table 1) Suppose that the robot has seen the user tidy a room.

Original: After the room becomes untidy, the robot must tidy up each object.
Revised: After the room becomes untidy, the robot must tidy up each object.

Original: This tests how well the networkcan reconstruct the scene based on the user preference vector and its prior knowledge.
Revised: This tests how well the networkcan reconstruct the scene based on the user preference vector and its prior knowledge.

Original: For eachscene separately, we trained NeatNet as described in Section 4.2.
Revised: For eachscene separately, we trained NeatNet as described in Section 4.2.

Original: Then for each test user we passtheir scene through the VAE and ask them to rate the reconstructed scene.
Revised: Then for each test user we passtheir scene through the VAE and ask them to rate the reconstructed scene.

Original: [Table]
Revised: [Table]

Original: NeatNet can reconstruct personalized tidy arrangements from a user preference vector, outperforming baselines and approaching the user’s handmade positive examples.
Revised: NeatNet can reconstruct personalised tidy arrangements from a user preference vector, outperforming baselines and approaching the user’s handmade Positive-Example .

Revised: In a direct comparison, all test users ranked NeatNet over Pose-Graph .

Original: Surprisingly, userssometimes prefer the reconstructed tidy scene over their own example .
Revised: Surprisingly, users sometimes even preferthe reconstructed tidy scene over their own example .

Original: This is because in each individual user-created arrangement, there is noise in how they placed each object (realistically thiswould also include error from localization/pose estimation).
Revised: This is because in each individual user-createdarrangement, there is noise in how they placed each object (realistically this would also include errorfrom localisation/pose estimation).

Original: Our method uses prior knowledge fromother users to correct for noise in individual examples (analogous to VAE image denoising [19]).
Revised: Our method uses prior knowledge from other users to correct fornoise in individual examples (analogous to VAE image denoising [21]).

Original: Can NeatNet Find a Home for a New Object?
Revised: Can NeatNet Find a Home for a New Object?

Original: (Table 2)
Revised: (Table 2)

Original: If a user brings a new object (e.g. acup) into the scene, the robot should predict a tidy position for it.
Revised: If a user brings a new object (e.g. acup) into the scene, the robot should predict a tidy position for it.

Original: NeatNet is trained as before, butwith the positions of random nodes masked out from input examples to encourage generalization.
Revised: NeatNet is trained as before, butwith the positions of random nodes masked out from input examples to encourage generalisation.

Original: At test time, we mask out the new object’s position from the test user’s example arrangement, but the position predictor must still place it.
Revised: At test time, we mask out the new object’s position from the test user’s example arrangement, butthe position predictor must still place it.

Original: This is analogous to the use of generative models for“inpainting” missing or obstructed regions of input images [20].
Revised: This is analogous to the use of generative models for“inpainting” missing or obstructed regions of input images [22].

Original: This experiment is repeated for eachobject below.
Revised: This experiment is repeated foreach object.

Original: NeatNet outperforms the baselines by successfully combining what it knows aboutthe current user’s preferences with prior knowledge of how similar users placed the new object.
Revised: NeatNet outperforms the baselines by successfully combining what it knows about thecurrent user’s preferences with prior knowledge of how similar users placed the new object.

Original: [Table]
Revised: [Table]

Original: Can NeatNet Generalize to Objects Unseen During Training?
Revised: Can NeatNet Generalise to Objects Unseen During Training?

Original: (Table 3)
Revised: (Table 3)

Original: The network must placea new blue box into an abstract scene, and a laptop into the ofﬁce scene, though it has never seena laptop during training.
Revised: The network must placea new blue box into an abstract scene, and a laptop into the ofﬁce scene, though it has never seena laptop during training.

Original: The training process is similar to the previous experiment.
Revised: The training process is similar to the previous experiment.

Original: NeatNet ’splacement of the new objects (box and laptop) is satisfactory to users.
Revised: NeatNet ’splacement of the new objects (box and laptop) is satisfactory to users.

Original: To place the laptop, NeatNetconsiders how the user placed several semantically similar objects: the computer, laptop and keyboard.
Revised: To place the laptop, NeatNetconsiders how the user placed several semantically similar objects: the computer, laptop and keyboard.

Original: By contrast, the Nearest-Neighbor baseline places the laptop based solely on how the userplaced their desktop computer, thus misplacing it under the desk.
Revised: The Nearest-Neighbour baseline places the laptop based solely on how the user placedtheir desktop computer, thus misplacing it under the desk.

Original: Tidying Method
Revised: Weighted-kNN-Regression performswell, but NeatNet was still ranked higher by every user because it can extract the semantic information from the word vectors which is most useful for tidying, since the network is trained end-to-end. Furthermore, NeatNet is able to learn and extrapolate the order-of-size pattern in the abstract scene.

Original: [Table]
Revised: [Table]

Original: Can NeatNet Predict a Personalized Arrangement for a New Scene?
Revised: Can NeatNet Predict a Personalised Arrangement for a New Scene?

Original: (Table 4)
Revised: (Table 4)

Original: At training time,the network is shown how the user has arranged both abstract scenes, with random scene maskingapplied to encourage generalization.
Revised: At training time,the network is shown how the user has arranged both abstract scenes, with random scene maskingapplied to encourage generalisation.

Original: At test time, the network is shown one abstract scene and askedto predict how the test user would arrange the other.
Revised: At test time, the network is shown one abstract scene and askedto predict how the test user would arrange the other.

Revised: kNN-Scene-Projection produces personalised arrangements, but NeatNet lines up objects more neatly because it combines personalisationwith learned prior knowledge about these objects from training users.

Original: We found that the Random-User baselineperforms moderately well, especially when by luck the random training user which was chosen hassimilar preferences to the test user.
Revised: The Random-User baselineperforms moderately well, especially when by luck the random training user which was chosen hassimilar preferences to the test user.

Original: However, when those preferences differ, the neural networkmethod out-performs it.
Revised: However, when those preferences differ, the neural networkmethod out-performs it.

Original: This shows the importance of accounting for preferences, and demonstratesthat the network can learn preferences which enable generalization across scenes.
Revised: This shows the importance of accounting for preferences, and demonstratesthat the network can learn preferences which enable generalisation across scenes.

Original: [Table]
Revised: [Table]

Original:  Conclusions
Revised:  Conclusions

Original: Findings.
Revised: Findings.

Original: Our NeatNet architecture learned latent representations of user spatial preferences bytraining as a Variational Autoencoder.
Revised: Our NeatNet architecture learned latent representations of user spatial preferences bytraining as a Variational Autoencoder.

Original: Graph Neural Network components allowed it to learn directly from user-arranged scenes, represented as graphs, and word embeddings enabled predictions for unseen objects.
Revised: Graph Neural Network components allowed it to learn directly from user-arranged scenes, represented as graphs, and word embeddings enabled predictions for unseen objects.

Original: NeatNet’s ability to generalize and make personalized predictions wasdemonstrated through experiments with real users, adding new capabilities to existing methods.
Revised: NeatNet’s ability to generalise and make personalised predictions wasdemonstrated through experiments with real users, adding new capabilities to existing methods.

Original: Future work.
Revised: Future work.

Original: Rather than using the word embedding of the object’s name, convolutional layers canbe used to generate a semantic embedding for an object based on its visual appearance (e.g. teacupslook similar to mugs and are placed similarly).
Revised: Rather than using the word embedding of the object’s name, convolutional layers canbe used to generate a semantic embedding for an object based on its visual appearance (e.g. teacupslook similar to mugs and are placed similarly).

Original: Furthermore, we can combine our approach withmodel-based priors from existing methods, e.g. using human pose context [9] as part of our lossfunction to ensure objects are placed conveniently, while also tailoring to individual preferences.
Revised: Furthermore, we can combine our approach withmodel-based priors from existing methods, e.g. using human pose context [11] as part of our lossfunction to ensure objects are placed conveniently, while also tailoring to individual preferences.