Original: ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Revised: ProcTHOR: Large-Scale Embodied AI Using Procedural Generation

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

Original: AffiliationAddress
Revised: AffiliationAddress

Original: Abstract
Revised: Abstract

Original: Massive datasets and high-capacity models have driven many recent advancementsin computer vision and natural language understanding.
Revised: Massive datasets and high-capacity models have driven many recent advancementsin computer vision and natural language understanding.

Original: This work presents a platform to enable similar success stories in Embodied AI.
Revised: This work presents a platform to enable similar success stories in Embodied AI.

Original: We propose P ROC THOR, aframework for procedural generation of Embodied AI environments.
Revised: We propose P ROC THOR, aframework for procedural generation of Embodied AI environments.

Original: P ROC THORenables us to sample arbitrarily large datasets of diverse, interactive, customizable,and performant virtual environments to train and evaluate embodied agents acrossnavigation, interaction, and manipulation tasks.
Revised: P ROC THORenables us to sample arbitrarily large datasets of diverse, interactive, customizable,and performant virtual environments to train and evaluate embodied agents acrossnavigation, interaction, and manipulation tasks.

Original: We demonstrate the power andpotential of P ROC THOR via a sample of 10,000 generated houses and a simpleneural model.
Revised: We demonstrate the power andpotential of P ROC THOR via a sample of 10,000 generated houses and a simpleneural model.

Original: Models trained using only RGB images on P ROC THOR, with noexplicit mapping and no human task supervision produce state-of-the-art resultsacross 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the presently running Habitat 2022, AI2-THOR Rearrangement2022, and RoboTHOR challenges.
Revised: Models trained using only RGB images on P ROC THOR, with noexplicit mapping and no human task supervision produce state-of-the-art resultsacross 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the presently running Habitat 2022, AI2-THOR Rearrangement2022, and RoboTHOR challenges 1 .

Original: We also demonstrate strong 0-shot results onthese benchmarks, via pre-training on P ROC THOR with no fine-tuning on thedownstream benchmark, often beating previous state-of-the-art systems that accessthe downstream training data.
Revised: We also demonstrate strong 0-shot resultson these benchmarks, via pre-training on P ROC THOR with no fine-tuning on thedownstream benchmark, often beating previous state-of-the-art systems that accessthe downstream training data.

Original:  Introduction
Revised:  Introduction

Original: Computer vision and natural language processing models have become increasingly powerful throughthe use of large-scale training data.
Revised: Computer vision and natural language processing models have become increasingly powerful throughthe use of large-scale training data.

Original: Recent models such as CLIP [32], DALL-E [34], GPT-3 [4] and
Revised: Recent models such as CLIP [35], DALL-E [37], GPT-3 [5] and

Original: Flamingo [1] use massive amounts of task agnostic data to pre-train large neural architectures thatperform remarkably well at downstream tasks, including in zero and few-shot settings.
Revised: Flamingo [4] use massive amounts of task agnostic data to pre-train large neural architectures thatperform remarkably well at downstream tasks, including in zero and few-shot settings.

Original: In comparison,the Embodied AI (E-AI) research community predominantly trains agents in simulators with farfewer scenes [33, 20, 10].
Revised: In comparison,the Embodied AI (E-AI) research community predominantly trains agents in simulators with farfewer scenes [36, 24, 13].

Original: Due to the complexity of tasks and the need for long planning horizons, thebest performing E-AI models continue to overfit on the limited training scenes and thus generalizepoorly to unseen environments.
Revised: Due to the complexity of tasks and the need for long planning horizons, thebest performing E-AI models continue to overfit on the limited training scenes and thus generalizepoorly to unseen environments.

Original: In recent years, E-AI simulators have become increasingly more powerful with support for physics,manipulators, object states, deformable objects, fluids, and real-sim counterparts [20, 35, 36, 15, 45],but scaling them up to tens of thousands of scenes has remained challenging.
Revised: In recent years, E-AI simulators have become increasingly more powerful with support for physics,manipulators, object states, deformable objects, fluids, and real-sim counterparts [24, 40, 41, 18, 51],but scaling them up to tens of thousands of scenes has remained challenging.

Original: Existing E-AIenvironments are either designed manually [20, 15] or obtained via 3D scans of real structures[35, 33].
Revised: Existing E-AIenvironments are either designed manually [24, 18] or obtained via 3D scans of real structures[40, 36].

Original: The former approach requires 3D artists to spend a significant amount of time designing 3Dassets, arranging them in sensible configurations within large spaces, and carefully configuring theright textures and lighting in these environments.
Revised: The former approach requires 3D artists to spend a significant amount of time designing 3Dassets, arranging them in sensible configurations within large spaces, and carefully configuring theright textures and lighting in these environments.

Original: The latter involves moving specialized camerasthrough many real-world environments and then stitching the resulting images together to form 3Dreconstructions of the scenes.
Revised: The latter involves moving specialized cameras through many real-world environments and then stitching the resulting images together to form 3Dreconstructions of the scenes.

Original: These approaches are not scalable, and expanding existing scenerepositories multiple orders of magnitude is not practical.
Revised: These approaches are not scalable, and expanding existing scenerepositories multiple orders of magnitude is not practical.

Original: We present P ROC THOR, a framework built off of AI2-THOR [20], to procedurally generate fullyinteractive, physics-enabled environments for E-AI research.
Revised: We present P ROC THOR, a framework built off of AI2-THOR [24], to procedurally generate fullyinteractive, physics-enabled environments for E-AI research.

Original: Given a room specification (e.g., ahouse with 3 bedrooms, 3 baths, and 1 kitchen), P ROC THOR can produce a large and diverse set offloorplans that meet these requirements (Fig. 1).
Revised: Given a room specification (e.g., ahouse with 3 bedrooms, 3 baths, and 1 kitchen), P ROC THOR can produce a large and diverse set offloorplans that meet these requirements (Fig. 1).

Original: A large asset library of 108 object types and 1633fully interactable instances is used to automatically populate each floorplan, ensuring that objectplacements are physically plausible, natural, and realistic.
Revised: A large asset library of 108 object types and 1633fully interactable instances is used to automatically populate each floorplan, ensuring that objectplacements are physically plausible, natural, and realistic.

Original: One can also vary the intensity and colorof lighting elements (both artificial lighting and simulated skyboxes) in each scene, to simulatevariations in indoor lighting and the time of the day.
Revised: One can also vary the intensity and colorof lighting elements (both artificial lighting and simulated skyboxes) in each scene, to simulatevariations in indoor lighting and the time of the day.

Original: Assets (such as furniture and fruit) and largerstructures such as walls and doors can be assigned a variety of colors and textures, sampled from setsof plausible colors and materials for each asset category.
Revised: Assets (such as furniture and fruit) and largerstructures such as walls and doors can be assigned a variety of colors and textures, sampled from setsof plausible colors and materials for each asset category.

Original: Together, the diversity of layouts, assets,placements, and lighting leads to an arbitrarily large set of environments – allowing P ROC THOR toscale orders of magnitude beyond the number of scenes currently supported by present-day simulators.
Revised: Together, the diversity of layouts, assets,placements, and lighting leads to an arbitrarily large set of environments – allowing P ROC THOR toscale orders of magnitude beyond the number of scenes currently supported by present-day simulators.

Original: In addition, P ROC THOR supports dynamic material randomizations, whereby colors and materialsof individual assets can be randomized each time an environment is loaded into memory for training.
Revised: In addition, P ROC THOR supports dynamic material randomizations, whereby colors and materialsof individual assets can be randomized each time an environment is loaded into memory for training.

Original: Importantly, in contrast to environments produced using 3D scans, scenes produced by P ROC THORcontain objects that both support a variety of different object states ( e.g. open, closed, broken, etc. )and are fully interactive so that they can be physically manipulated by agents with robotic arms.
Revised: Importantly, in contrast to environments produced using 3D scans, scenes produced by P ROC THORcontain objects that both support a variety of different object states ( e.g. open, closed, broken, etc. )and are fully interactive so that they can be physically manipulated by agents with robotic arms.

Original: We also present A RCHITEC THOR, a 3D artist-designed set of 10 high quality fully interactablehouses, meant to be used as a test-only environment for research within household environments.
Revised: We also present A RCHITEC THOR, a 3D artist-designed set of 10 high quality fully interactablehouses, meant to be used as a test-only environment for research within household environments.

Original: In contrast to AI2-iTHOR (single rooms) and RoboTHOR (lesser visual diversity) environments,
Revised: In contrast to AI2-iTHOR (single rooms) and RoboTHOR (lesser visual diversity) environments,

Original: A RCHITEC THOR contains larger, diverse, and realistic houses.
Revised: A RCHITEC THOR contains larger, diverse, and realistic houses.

Original: We demonstrate the ease and effectiveness of P ROC THOR by sampling an environment of 10,000houses (named P ROC THOR-10 K ), composed of diverse layouts ranging from small 1-room housesto larger 10-room houses.
Revised: We demonstrate the ease and effectiveness of P ROC THOR by sampling an environment of 10,000houses (named P ROC THOR-10 K ), composed of diverse layouts ranging from small 1-room housesto larger 10-room houses.

Original: We train agents with very simple neural architectures (CNN+RNN) –without a depth sensor, and instead only employing RGB channels, with no explicit mapping andno human task supervision – on P ROC THOR-10 K and produce state-of-the-art (SoTA) models onseveral navigation and interaction benchmarks. As of 10am PT on June 14th, 2022 we obtain (1) RoboTHOR ObjectNav Challenge [3] – 0-shot performance  superior to the previous SoTA whichuses RoboTHOR training scenes – with fine-tuning we obtain an 8.8 point improvement in SPL overthe previous SoTA; (2) Habitat ObjectNav Challenge 2022 [27] – top of the leaderboard resultswith a > 3 point gain in SPL over the next best submission; (3) 1-phase Rearrangement Challenge2022 [2] – top of the leaderboard results with Prop Fixed Strict improving from 0.19 to 0.245; (4) 
Revised: We train agents with very simple neural architectures (CNN+RNN) –without a depth sensor, and instead only employing RGB channels, with no explicit mapping andno human task supervision – on P ROC THOR-10 K and produce state-of-the-art (SoTA) models onseveral navigation and interaction benchmarks including (1) RoboTHOR ObjectNav Challenge [3]– Our 0-shot performance is superior to the previous best model which uses RoboTHOR trainingscenes, and fine-tuning improves this more; (2) Habitat ObjectNav Challenge 2022 [1] – Topof the leaderboard, as of the submission deadline with 6% Success improvement over the nextbest submission; (3) 1-phase Rearrangement Challenge 2022 [2] – Top of the leaderboard, asof the submission deadline with Prop Fixed Strict improving from 0.19 to 0.245; (4) AI2-iTHOR

Original: AI2-iTHOR ObjectNav –  0-shot numberswhich already outperform a previous model that trains on AI2-iTHOR, with fine-tuning we achieve a success rate of 77.5%; (5) ArmPointNav [12] – 0-shotnumber that beats previous SoTA results when using RGB; and (6) ArchitecTHOR ObjectNav – alarge success rate improvement from 18.5% to 31.4%.
Revised: ObjectNav – Our 0-shot numbers already outperform a previous model that trains on AI2-iTHORwith fine-tuning achieving Success up to 77.5%; (5) ArmPointNav [17] – Our 0-shot numbers beatprevious SoTA results when using RGB; and (6) ArchitecTHOR ObjectNav – A large Successimprovement from 18.5% to 31.4%.

Original: Finally, an ablation analysis clearly shows theadvantages of scaling up from 10 to 100 to 1K and finally to 10K scenes and indicates that furtherimprovements can be obtained by invoking P ROC THOR to produce even larger environments.
Revised: Finally, an ablation analysis clearly shows the advantages ofscaling up from 10 to 100 to 1K and finally to 10K scenes and indicates that further improvementscan be obtained by invoking P ROC THOR to produce even larger environments.

Original: In summary, our contributions are (1) P ROC THOR, a framework that allows for the performantprocedural generation of an unbounded number of diverse, fully-interactive, simulated environments,(2)
Revised: In summary, our contributions are (1) P ROC THOR, a framework that allows for the performantprocedural generation of an unbounded number of diverse, fully-interactive, simulated environments,(2)

Original: A RCHITEC THOR, a new, 3D artist-designed set of houses for E-AI evaluation, and (3) SoTAresults across six E-AI benchmarks covering manipulation and navigation tasks, including strong
Revised: A RCHITEC THOR, a new, 3D artist-designed set of houses for E-AI evaluation, and (3) SoTAresults across six E-AI benchmarks covering manipulation and navigation tasks, including strong0-shot results.

Original: [Table]
Revised: P ROC THOR will be open-sourced and the code used in this work will be released.

Original:  Related Work
Revised:  Related Work

Original: Embodied AI platforms.
Revised: Embodied AI platforms.

Original: Various Embodied AI platforms have been developed over the past severalyears [20, 35, 36, 45, 15, 43].
Revised: Various Embodied AI platforms have been developed over the past severalyears [24, 40, 41, 51, 18, 49].

Original: These platforms target different design goals.
Revised: These platforms target different design goals.

Original: AI2-THOR [20] andits variants (ManipulaTHOR [12] and RoboTHOR [10]) are built in the Unity game engine andfocus on agent-object interactions, object state changes, and accurate physics simulation.
Revised: AI2-THOR [24] andits variants (ManipulaTHOR [17] and RoboTHOR [13]) are built in the Unity game engine andfocus on agent-object interactions, object state changes, and accurate physics simulation.

Original: Unlike
Revised: Unlike

Original: AI2-THOR, Habitat [35] provides scenes constructed from 3D scans of houses, however, objects andscenes are not interactable.
Revised: AI2-THOR, Habitat [40] provides scenes constructed from 3D scans of houses, however, objects andscenes are not interactable.

Original: A more recent version, Habitat 2.0 [38], introduces object interactionsat the expense of being limited to one floorplan and synthetic scenes.
Revised: A more recent version, Habitat 2.0 [43], introduces object interactionsat the expense of being limited to one floorplan and synthetic scenes.

Original: iGibson [36] includes photorealistic scenes, but with limited interactions such as pushing.
Revised: iGibson [41] includes photorealistic scenes, but with limited interactions such as pushing.

Original: iGibson 2.0 [22] extends iGibson byfocusing on household tasks and object state changes in synthetic scenes and includes a virtual realityinterface.
Revised: iGibson 2.0 [26] extends iGibson byfocusing on household tasks and object state changes in synthetic scenes and includes a virtual realityinterface.

Original: ThreeDWorld
Revised: ThreeDWorld

Original: [15] targets high-fidelity physics simulation such as liquid and deformableobject simulation.
Revised: [18] targets high-fidelity physics simulation such as liquid and deformableobject simulation.

Original: VirtualHome [31] is designed for simulating human activities via programs.
Revised: VirtualHome [34] is designed for simulating human activities via programs.

Original: RLBench [18], RoboSuite [50] and Sapien [45] target fine-grained manipulation.
Revised: RLBench [20], RoboSuite [55] and Sapien [51] target fine-grained manipulation.

Original: The main advantageof P ROC THOR is that we can generate a diverse set of interactive scenes procedurally, enablingstudies of data augmentation and large-scale training in the context of Embodied AI.
Revised: The main advantageof P ROC THOR is that we can generate a diverse set of interactive scenes procedurally, enablingstudies of data augmentation and large-scale training in the context of Embodied AI.

Original: Large-scale datasets.
Revised: Large-scale datasets.

Original: Large-scale datasets have resulted in major breakthroughs in different domains such as image classification [11, 21], vision and language [8, 39], 3D understanding [6, 46],autonomous driving [5, 37], and robotic object manipulation [30, 28].
Revised: Large-scale datasets have resulted in major breakthroughs in different domainssuch as image classification [14, 25], vision and language [11, 44], 3D understanding [8, 52],autonomous driving [6, 42], and robotic object manipulation [33, 30].

Original: However, there are not manyinteractive large-scale datasets for Embodied AI research.
Revised: However, there are not manyinteractive large-scale datasets for Embodied AI research.

Original: P ROC THOR includes interactive housesgenerated procedurally.
Revised: P ROC THOR includes interactive housesgenerated procedurally.

Original: Hence, there are an arbitrarily large number of scenes in the framework.
Revised: Hence, there are an arbitrarily large number of scenes in the framework.

Original: Theclosest works to ours are [33, 29, 23].
Revised: Theclosest works to ours are [36, 32, 28].

Original: HM3D [33] is a recent framework that includes 1,000 scenesgenerated using 3D scans of real environments.
Revised: HM3D [36] is a recent framework that includes 1,000 scenesgenerated using 3D scans of real environments.

Original: P ROC THOR has a number of key distinctions: (1)unlike HM3D which includes static scenes, the scenes in P ROC THOR are interactive i.e., objectscan move and change state, the lighting and texture of objects can change, and a physics enginedetermines the future states of the scenes; (2) it is challenging to scale up HM3D as it requiresscanning a house and cleaning up the data, while we can procedurally generate more houses; (3)
Revised: P ROC THOR has a number of key distinctions: (1)unlike HM3D which includes static scenes, the scenes in P ROC THOR are interactive i.e., objectscan move and change state, the lighting and texture of objects can change, and a physics enginedetermines the future states of the scenes; (2) it is challenging to scale up HM3D as it requiresscanning a house and cleaning up the data, while we can procedurally generate more houses; (3)

Original: HM3D can be used only for navigation tasks (as there is no physics simulation and object interaction),while P ROC THOR can be used for tasks other than navigation.
Revised: HM3D can be used only for navigation tasks (as there is no physics simulation and object interaction),while P ROC THOR can be used for tasks other than navigation.

Original: OpenRooms [23] is similar to HM3Din terms of the source of the data (3D scans) and dataset size.
Revised: OpenRooms [28] is similar to HM3Din terms of the source of the data (3D scans) and dataset size.

Original: However, OpenRooms is interactive.
Revised: However, OpenRooms is interactive.

Original: OpenRooms is also confined to the set of scanned houses, and it takes a significant amount of timeto annotate a new scene (e.g., labeling materials for one object takes 1 minute), while P ROC THORdoes not suffer from these issues.
Revised: OpenRooms is also confined to the set of scanned houses, and it takes a significant amount of timeto annotate a new scene (e.g., labeling materials for one object takes 1 minute), while P ROC THORdoes not suffer from these issues.

Original: Megaverse [29] is another large-scale Embodied AI platform thatincludes procedurally generated environments.
Revised: Megaverse [32] is another large-scale Embodied AI platform thatincludes procedurally generated environments.

Original: Although it is impressive in terms of simulation speed,it includes only game-like environments with a simplified appearance.
Revised: Although it is impressive in terms of simulation speed,it includes only game-like environments with a simplified appearance.

Original: In contrast, P ROC THORmimics real-world houses in terms of the complexity of appearance, physics, and object interactions.
Revised: In contrast, P ROC THORmimics real-world houses in terms of the complexity of appearance, physics, and object interactions.

Original: Scene synthesis.
Revised: Scene generation.

Original: Work on scene synthesis is typically broken down into generating floorplans [25,26, 17, 42] and sampling object placement in rooms [13, 16, 48, 49, 9].
Revised: Indoor scene synthesis has been studied extensively in computer vision andgraphics communities. [9, 10, 7] address generating 3D scenes from text descriptions. [48, 19, 31]learn to generate house floorplans.

Original: Our work aimed to generatediverse and semantically plausible houses using the best existing approaches or building on existingworks in areas that were insufficient for our use case.
Revised: [38, 53, 12, 45, 21, 27] use generative models for indoor scenegeneration. [54, 39] propose potential objects for a query location in an indoor scene. Othershave used procedural generation [22, 16] and unsupervised learning [15] to synthesize grid-worldenvironments for AI.

Original: Our floorplan generation process is adaptedfrom [25, 26], which takes ina high-level specification of the rooms in a house and their connectivityconstraints, and randomly generates floorplans satisfying these constraints. Our object placement ismost similar to [49, 16, 48, 47, 7], where we iteratively place objects on floors, walls, and surfacesand use semantic asset groups to sample objects that co-occur ( e.g. chairs next to tables).
Revised: P ROC THOR is specifically designed for Embodied AI research in the sensethat (1) all scenes are interactive and physics-enabled, and the placement of objects respects thephysics of the world (e.g., there are no two objects that clip through each other), (2) there are variousforms of scene augmentation such as randomization of object placements while following certaincommonsense rules, variation in the appearance of objects and structures, and variation in lighting. [Equation]

Original: Themodular generation process used in this work makes it easy to swap in and update any stage of ourhouse generation pipeline with a better algorithm. In this work, we found the procedural generationapproaches to be more reliable and flexible than the ones based on deep learning when adaptingit to our custom object database and when generating more complex houses that were out of thedistribution of static house datasets [14, 42, 24]. For a more detailed comparison, including adiscussion of some of the limitations of deep learning approaches, please refer to the Appendix.
Revised: [Figure]

Original: P ROC THOR is a framework to procedurally generate E-AI environments.
Revised: P ROC THOR is a framework to procedurally generate E-AI environments.

Original: It extends AI2-THORand, thereby, inherits AI2-THOR’s large asset library, robotic agents, and accurate physics simulation.
Revised: It extends AI2-THORand, thereby, inherits AI2-THOR’s large asset library, robotic agents, and accurate physics simulation.

Original: Just as in scenes painstakingly created by designers in AI2-THOR, environments in P ROC THORare fully interactive and support navigation, object manipulation, and multi-agent interaction. 
Revised: Just as in scenes painstakingly created by designers in AI2-THOR, environments in P ROC THORare fully interactive and support navigation, object manipulation, and multi-agent interaction. [Figure] [Figure]

Original: Fig.
Revised: Fig.

Original: 2 shows a high-level schematic of the procedure used by P ROC THOR to generate a scene.
Revised: 2 shows a high-level schematic of the procedure used by P ROC THOR to generate a scene.

Original: Givena room specification ( e.g. house with 1 bedroom + 1 bathroom), we use multi-stage conditionalsampling to, iteratively, generate a floor plan, create an external wall structure, sample lighting,and doors, then sample assets including large, small and wall objects, pick colors  and textures, anddetermine appropriate placements for assets within the scene.
Revised: Given a room specification ( e.g. house with 1 bedroom + 1 bathroom), P ROC THOR uses multi-stageconditional sampling to, iteratively, generate a floor plan, create an external wall structure, samplelighting, and doors, then sample assets including large, small and wall objects, pick colors andtextures, and determine appropriate placements for assets within the scene.

Original: We refer the reader to the appendixfor details regarding our procedural generation and sampling mechanism, but highlight five keycharacteristics of P ROC THOR:
Revised: We refer the reader tothe appendix for details regarding our procedural generation and sampling mechanism, but highlightfive key characteristics of P ROC THOR:

Original: Diversity , Interactivity , Customizability , Scale , and Efficiency .
Revised: Diversity , Interactivity , Customizability , Scale , and Efficiency .

Original: [Figure]

Original: [Figure]

Original: Diversity.
Revised: Diversity.

Original: P ROC THOR enables the creation of rich and diverse environments.
Revised: P ROC THOR enables the creation of rich and diverse environments.

Original: Mirroring the successof pre-training models with diverse data in the vision and NLP domains, we demonstrate the utility ofthis diversity on several E-AI tasks.
Revised: Mirroring the successof pre-training models with diverse data in the vision and NLP domains, we demonstrate the utility ofthis diversity on several E-AI tasks.

Original: Scenes in P ROC THOR exhibit diversity across several facets:
Revised: Scenes in P ROC THOR exhibit diversity across several facets:

Original: Diversity of floor plans.
Revised: Diversity of floor plans.

Original: Given a room specification, we first employ iterative boundary cuttingto obtain an external scene layout (that can range from a simple rectangle to a complex polygon).
Revised: Given a room specification, we first employ iterative boundary cuttingto obtain an external scene layout (that can range from a simple rectangle to a complex polygon).

Original: The recursive layout generation algorithm by Lopes
Revised: The recursive layout generation algorithm by Lopes

Original: et al.
Revised: et al.

Original: [25] is then used to divide the scene intothe desired rooms.
Revised: [29] is then used to divide the scene intothe desired rooms.

Original: Finally, we determine connectivity between rooms using a set of user-definedconstraints.
Revised: Finally, we determine connectivity between rooms using a set of user-definedconstraints.

Original: These procedures result in natural room layouts (e.g., bedrooms are often connected toadjoining bathrooms via a door, bathrooms more often have a single entrance, etc).
Revised: These procedures result in natural room layouts (e.g., bedrooms are often connected toadjoining bathrooms via a door, bathrooms more often have a single entrance, etc).

Original: As exemplified in
Revised: As exemplified in

Original: Fig.
Revised: Fig.

Original: 3, P ROC THOR generates hugely diverse floor plans using this procedure.
Revised: 3, P ROC THOR generates hugely diverse floor plans using this procedure.

Original: [Figure]
Revised: Diversity of assets.

Original: [Figure] P ROC THOR populates scenes with small and large assets from its database of1633 household assets across 108 categories (examples in Fig. 4).
Revised: P ROC THOR populates scenes with small and large assets from its database of1633 household assets across 108 categories (examples in Fig. 4).

Original: While many assets are inheritedfrom AI2-THOR, we also introduce new assets such as windows, doors, and countertops, handdesigned by 3D graphic designers.
Revised: While many assets are inheritedfrom AI2-THOR, we also introduce new assets such as windows, doors, and countertops, handdesigned by 3D graphic designers.

Original: Asset instances are split into train/val/test subsets and areinteractable, i.e. objects can be picked and placed within the scenes, some objects have multiplestates ( e.g. a light can be on or off) and several objects consists of parts with rigid body motions ( e.g.door on a microwave).
Revised: Asset instances are split into train/val/test subsets and are interactable, i.e. objects can be picked and placed within the scenes, some objects have multiplestates ( e.g. a light can be on or off) and several objects consists of parts with rigid body motions ( e.g.door on a microwave).

Original: Diversity of materials.
Revised: Diversity of materials.

Original: Walls can have two kinds of materials – one of 40 solid (and popular) colorsor one of 122 wall textures such as brick and tile.
Revised: Walls can have two kinds of materials – one of 40 solid (and popular) colorsor one of 122 wall textures such as brick and tile.

Original: We also provide 55 floor materials.
Revised: We also provide 55 floor materials.

Original: The ceilingmaterial for the entire house is sampled from the set of wall materials.
Revised: The ceilingmaterial for the entire house is sampled from the set of wall materials.

Original: P ROC THOR also providesthe ability to randomize materials of objects.
Revised: P ROC THOR also providesthe ability to randomize materials of objects.

Original: Materials are only randomized within categories, whichensures objects still look and behave like the class they represent.
Revised: Materials are only randomized within categories, whichensures objects still look and behave like the class they represent.

Original: Diversity of object placements.
Revised: Diversity of object placements.

Original: Asset categories have several soft annotations that help place themrealistically within a house.
Revised: Asset categories have several soft annotations that help place themrealistically within a house.

Original: These include room assignments ( e.g. couch in a living room but not abathroom) and location assignments ( e.g. fridge along a wall, TV not on the floor).
Revised: These include room assignments ( e.g. couch in a living room but not abathroom) and location assignments ( e.g. fridge along a wall, TV not on the floor).

Original: We also developthe notion of a Semantic Asset Group (SAG) – groups of assets that typically co-occur ( e.g. diningtable with four chairs) and thus must be sampled and placed using dependent sampling.
Revised: We also developthe notion of a Semantic Asset Group (SAG) – groups of assets that typically co-occur ( e.g. diningtable with four chairs) and thus must be sampled and placed using dependent sampling.

Original: Given alayout, individual assets and SAGs that lie on the floor are sampled and placed iteratively, ensuringthat rooms continue to have adequate floor space for agents to navigate and manipulate objects.
Revised: Given alayout, individual assets and SAGs that lie on the floor are sampled and placed iteratively, ensuringthat rooms continue to have adequate floor space for agents to navigate and manipulate objects.

Original: Thenwall objects such as windows and paintings get placed, and finally, surface objects (ones found on topof other assets) are placed ( e.g. cups on the kitchen counter).
Revised: Thenwall objects such as windows and paintings get placed, and finally, surface objects (ones found on topof other assets) are placed ( e.g. cups on the kitchen counter).

Original: This sampling allows for a large anddiverse set of object choices and placements within any layout.
Revised: This sampling allows for a large anddiverse set of object choices and placements within any layout.

Original: Fig.
Revised: Fig.

Original: 6 shows such variations.
Revised: 6 shows such variations.

Original: Diversity of lighting.
Revised: Diversity of lighting.

Original: P ROC THOR supports a single directional light (analogous to the sun) andseveral point lights (analogous to lightbulbs).
Revised: P ROC THOR supports a single directional light (analogous to the sun) andseveral point lights (analogous to lightbulbs).

Original: Varying the color, intensity, and placement of thesesources allows us to simulate different artificial lighting, typically observed in houses, and also atdifferent times of the day.
Revised: Varying the color, intensity, and placement of these sources allows us to simulate different artificial lighting, typically observed in houses, and also atdifferent times of the day.

Original: Lighting has a significant effect on the rendered images as seen in Fig.
Revised: Lighting has a significant effect on the rendered images as seen in Fig.

Revised: [Figure]

Original: [Figure]
Revised: [Figure]

Original: Interactivity.
Revised: Interactivity.

Original: A key property of P ROC THOR is the ability to interact with objects to change theirlocation or state (Fig.12).
Revised: A key property of P ROC THOR is the ability to interact with objects to change theirlocation or state (Fig.

Original: This capability is fundamental to many Embodied AI tasks.
Revised: This capability is fundamental to many Embodied AI tasks.

Original: Datasetslike HM3D [33] that are created from static 3D scans do not possess this capability.
Revised: Datasetslike HM3D [36] that are created from static 3D scans do not possess this capability.

Original: P ROC THORsupports agents with arms capable of manipulating objects and interacting with each other. 
Revised: P ROC THORsupports agents with arms capable of manipulating objects and interacting with each other. [Figure]

Original: Customizability.
Revised: Customizability.

Original: P ROC THOR supports many room, asset, material, and lighting specifications.
Revised: P ROC THOR supports many room, asset, material, and lighting specifications.

Revised: [Figure]

Original: With a few simple lines of specification, one can easily generate customized environments of interest.
Revised: With a few simple lines of specification, one can easily generate customized environments of interest.

Revised: [Figure]

Original: Fig.
Revised: Fig.

Original: 9 shows examples of such varied scenes (classroom, library, and office). 
Revised: 9 shows examples of such varied scenes (classroom, library, and office). [Figure] [Figure]

Original: Scale and Efficiency.
Revised: Scale and Efficiency.

Original: P ROC THOR currently uses 16 different scene specifications to seed the scenegeneration process.
Revised: P ROC THOR currently uses 16 different scene specifications to seed the scene [Figure] generation process.

Original: These can result in over 100 billion layouts.
Revised: These can result in over 100 billion layouts.

Original: P ROC THOR uses 18 different
Revised: P ROC THOR uses 18 different

Original: Semantic Asset groups and 1633 assets.
Revised: Semantic Asset groups and 1633 assets.

Original: These can result in roughly 20 million unique asset groups.
Revised: These can result in roughly 20 million unique asset groups.

Original: Each of these assets can be placed in numerous locations.
Revised: Each of these assets can be placed in numerous locations.

Original: In addition, each house gets scaled anduses a variety of lighting.
Revised: In addition, each house gets scaled anduses a variety of lighting.

Original: This diversity of layouts, assets, materials, placements, and lighting enablesthe generation of arbitrarily large sets of houses – either statically generated and stored as a datasetor dynamically generated at each iteration of training.
Revised: This diversity of layouts, assets, materials, placements, and lighting enablesthe generation of arbitrarily large sets of houses – either statically generated and stored as a datasetor dynamically generated at each iteration of training.

Original: Scenes are efficiently represented in a JSONspecification and are loaded into AI2-THOR at runtime, making the memory overhead of storinghouses incredibly efficient.
Revised: Scenes are efficiently represented in a JSONspecification and are loaded into AI2-THOR at runtime, making the memory overhead of storinghouses incredibly efficient.

Original: Scene generation  is fully automatic and fast and P ROC THOR provideshigh framerates for training E-AI models (see Sec. 
Revised: Moreover, the scene generation process is fully automatic and fast and P ROC THOR provides high framerates for training E-AI models (see Sec. 4 for details). [Figure]

Original: 4 for details).
Revised: [Equation]

Original: We demonstrate the power and potential of P ROC THOR using a sampled set of 10,000 fully interactive houses obtained by the procedural generation process described in Section 3 – which welabel P ROC THOR-10K.
Revised: We demonstrate the power and potential of P ROC THOR using a sampled set of 10,000 fully interactive houses obtained by the procedural generation process described in Section 3 – which welabel P ROC THOR-10K.

Original: An additional set of 1,000 validation and 1,000 testing houses are availablefor evaluation.
Revised: An additional set of 1,000 validation and 1,000 testing houses are available [Figure] for evaluation.

Original: Asset splits across train/val/test are detailed in the Appendix.
Revised: Asset splits across train/val/test are detailed in the Appendix.

Original: All houses are fullynavigable, allowing an agent to traverse through each room without any interaction.
Revised: All houses are fullynavigable, allowing an agent to traverse through each room without any interaction.

Original: In terms of scale,
Revised: In terms of scale,

Original: [Table] P ROC THOR-10K is one of the largest sets of interactive home environments for Embodied AI – as acomparison, AI2-iTHOR [20] includes 120 scenes, RoboTHOR [10] has 89 scenes, iGibson [36] has15 scenes, Habitat Matterport 3D [33] has 1,000 static (non-interactive) scenes, and Habitat 2.0 [38]has 105 scene layouts.
Revised: P ROC THOR-10K is one of the largest sets of interactive home environments for Embodied AI – as acomparison, AI2-iTHOR [24] includes 120 scenes, RoboTHOR [13] has 89 scenes, iGibson [41] has15 scenes, Habitat Matterport 3D [36] has 1,000 static (non-interactive) scenes, and Habitat 2.0 [43]has 105 scene layouts.

Original: Scaling beyond 10K houses is straightforward and inexpensive.
Revised: Scaling beyond 10K houses is straightforward and inexpensive.

Original: This setof 10K houses was generated in 1 hour on a local workstation with 4 NVIDIA RTX A5000 GPUs.
Revised: This setof 10K houses was generated in 1 hour on a local workstation with 4 NVIDIA RTX A5000 GPUs.

Original: Fig.
Revised: Fig.

Original: 11 shows examples of ego-centric and top-down views of houses present in P ROC THOR-10K.
Revised: 11 shows examples of ego-centric and top-down views of houses present in P ROC THOR-10K.

Original: Scene statistics.
Revised: Scene statistics.

Original: Houses in P ROC THOR-10K are generated using 16 different room specifications.
Revised: Houses in P ROC THOR-10K are generated using 16 different room specifications.

Original: An example room spec is: A house with 1 bedroom connected to 1 bathroom, 1 kitchen, and 1living room and is visualized in Fig.
Revised: An example room spec is: A house with 1 bedroom connected to 1 bathroom, 1 kitchen, and 1living room and is visualized in Fig.

Original: Houses in this dataset have as few as 1 room and as manyas 10.
Revised: Houses in this dataset have as few as 1 room and as manyas 10.

Original: Fig.
Revised: Fig.

Original: 10 shows the distribution of areas (middle) and the number of rooms (right) of thesegenerated houses.
Revised: 10 shows the distribution of areas (middle) and the number of rooms (right) of thesegenerated houses.

Original: Our use of room specifications enables us to change the distribution of the size andcomplexity of houses fairly easily.
Revised: Our use of room specifications enables us to change the distribution of the size andcomplexity of houses fairly easily.

Original: P ROC THOR-

Revised: P ROC THOR-

Original: K encompasses a wider spectrum of scenes than
Revised: K encompasses a wider spectrum of scenes than

Original: AI2-iTHOR [20] and R OBO THOR [10] (biased towards room-sized scenes) and Gibson [44] and
Revised: AI2-iTHOR [24] and R OBO THOR [13] (biased towards room-sized scenes) and Gibson [50] and

Original: HM3D [33] (biased towards large houses).
Revised: HM3D [36] (biased towards large houses).

Original: Rooms in each of these houses contain objects from 95 different categories including commonhousehold objects such as fridges, countertops, beds, toilets, and house plants, and structure objectssuch as doorways and windows.
Revised: Rooms in each of these houses contain objects from 95 different categories including commonhousehold objects such as fridges, countertops, beds, toilets, and house plants, and structure objectssuch as doorways and windows.

Original: Fig.
Revised: Fig.

Original: 10 (left) shows the distribution of the number of objects perroom per house, which shows that houses in P ROC THOR-10K are well populated.
Revised: 10 (left) shows the distribution of the number of objects perroom per house, which shows that houses in P ROC THOR-10K are well populated.

Original: They also containobjects sampled via 18 different Semantic Asset groups.
Revised: They also containobjects sampled via 18 different Semantic Asset groups.

Original: Examples of Semantic asset groups (SAG)are a Dining Table with 4 Chairs or Bed with 2 Pillows .
Revised: Examples of Semantic asset groups (SAG)are a Dining Table with 4 Chairs or Bed with 2 Pillows .

Original: Given our large asset library and SAGs, wecan create 19.3 million combinations of group instantiations. 
Revised: Given our large asset library and SAGs, wecan create 19.3 million combinations of group instantiations. [Table] FPS [Table]

Original: Rendering speed.
Revised: Rendering speed.

Original: A crucial requirement for large-scale training is high rendering speed since thetraining algorithms require millions of iterations to converge.
Revised: A crucial requirement for large-scale training is high rendering speed since thetraining algorithms require millions of iterations to converge.

Original: Table 1 shows these statistics.
Revised: Table 1 shows these statistics.

Original: Experiments were run on a server with 8 NVIDIA Quadro RTX 8000 GPUs.
Revised: Experiments were run on a server with 8 NVIDIA Quadro RTX 8000 GPUs.

Original: For the 1 GPU experiments,we use 15 processes and for the 8 GPU experiments, we use 120 processes, evenly distributed acrossthe GPUs.
Revised: For the 1 GPU experiments,we use 15 processes and for the 8 GPU experiments, we use 120 processes, evenly distributed acrossthe GPUs.

Original: P ROC THOR provides framerates comparable to iTHOR and RoboTHOR environmentsin spite of having larger houses (See Appendix for details), rendering it fast enough for training largemodels for hundreds of millions of steps in a reasonable amount of time.[Figure]
Revised: P ROC THOR provides framerates comparable to iTHOR and RoboTHOR environmentsin spite of having larger houses (See Appendix for details), rendering it fast enough for training largemodels for hundreds of millions of steps in a reasonable amount of time.

Original: [Figure]

Original: In order to test if models trained on ProcTHOR can generalize to real-world floorplans and objectplacements, a test set of houses was needed.

Original: Neither iTHOR (single room scenes) nor RoboTHOR(dorm-sized maze-styled scenes) contain scenes that are representative of real-world homes.

Original: Therefore, we worked with professional 3D artists to create ArchitecTHOR, which contains 10 evaluationhouses (5 val, 5 test) that mimick the style of real-world homes.

Original: ArchitecTHOR val houses containbetween 4-8 rooms, 121 ± 26 objects per house, and a typical floor size of 111 ± 26 m 2 .

Original: By comparison, P ROC THOR-10K houses have a much higher variance, with between 1-10 rooms, 76 ± 48objects per house, and a typical floor size of 96 ± 74 m 2 .

Original:  Experiments
Revised:  Experiments

Original: Tasks.
Revised: Tasks.

Original: We now present results for models pre-trained on P ROC THOR-10K on several navigation andmanipulation benchmarks to demonstrate the benefits of large-scale training.
Revised: We now present results for models pre-trained on P ROC THOR-10K on several navigation andmanipulation benchmarks to demonstrate the benefits of large-scale training.

Original: We consider ObjectNav(navigation towards a specific object category) in P ROC THOR, A RCHITEC THOR, RoboTHOR [10],
Revised: We consider ObjectNav(navigation towards a specific object category) in P ROC THOR, A RCHITEC THOR, RoboTHOR [13],

Original: HM3D [33], and AI2-iTHOR [20].
Revised: HM3D [36], and AI2-iTHOR [24].

Original: We also consider two manipulation-based tasks: ArmPointNav [12] and 1-phase Room Rearrangement [40].
Revised: We also consider two manipulation-based tasks: ArmPointNav [17] and 1-phase Room Rearrangement [46].

Original: In ArmPointNav, the agent moves an object usinga robotic arm from a source location to a destination location specified in the 3D coordinate frame.
Revised: In ArmPointNav, the agent moves an object usinga robotic arm from a source location to a destination location specified in the 3D coordinate frame.

Original: In
Revised: In

Original: Room Rearrangement, the goal is to move objects or change their state to reach a target scene state.
Revised: Room Rearrangement, the goal is to move objects or change their state to reach a target scene state.

Original: Models.
Revised: Models.

Original: Our models for all tasks consist of a CNN to encode visual information and a GRU to capturetemporal information.
Revised: Our models for all tasks consist of a CNN to encode visual information and a GRU to capturetemporal information.

Original: We deliberately use a simple architecture across all tasks to show the benefitsof large-scale training.
Revised: We deliberately use a simple architecture across all tasks to show the benefitsof large-scale training.

Original: Our ObjectNav and Rearrangement models use the CLIP-based architecturesof [19].
Revised: Our ObjectNav and Rearrangement models use the CLIP-based architecturesof [23].

Original: Our ArmPointNav model uses a simpler visual encoder with 3 convolutional layers; we foundthis more effective than the CLIP encoder.
Revised: Our ArmPointNav model uses a simpler visual encoder with 3 convolutional layers; we foundthis more effective than the CLIP encoder.

Original: All models are trained with the AllenAct [41] framework,see the Appendix for training details.
Revised: All models are trained with the AllenAct [47] framework,see the Appendix for training details.

Original: Results.
Revised: Results.

Original: We present results in two settings: zero-shot and after fine-tuning on the training scenesprovided by the downstream benchmark.
Revised: We present results in two settings: zero-shot and after fine-tuning on the training scenesprovided by the downstream benchmark.

Original: Zero-shot experiments show us how well models trainedon P ROC THOR generalize to new environments, whereas fine-tuning experiments tell us if representations learned from P ROC THOR can serve as a good initialization for quick tuning.
Revised: Zero-shot experiments show us how well models trainedon P ROC THOR generalize to new environments, whereas fine-tuning experiments tell us if representations learned from P ROC THOR can serve as a good initialization for quick tuning.

Original: For allexperiments, we use only RGB images (no depth and other modalities is used).
Revised: For allexperiments, we use only RGB images (no depth and other modalities is used).

Original: Zero-shot is particularly challenging since other environments have different appearance statistics,layouts, and object distributions compared to P ROC THOR.
Revised: Zero-shot is particularly challenging since other environments have different appearance statistics,layouts, and object distributions compared to P ROC THOR.

Original: A RCHITEC THOR and AI2-iTHOR [20]are high-fidelity artist-designed scenes with high-quality shadows and lighting.
Revised: A RCHITEC THOR and AI2-iTHOR [24]are high-fidelity artist-designed scenes with high-quality shadows and lighting.

Original: HM3D is constructedfrom 3D scans of houses which can differ quite a bit from synthetic environments.
Revised: HM3D is constructedfrom 3D scans of houses which can differ quite a bit from synthetic environments.

Original: RoboTHOR [10]houses use wall panels and floors with very specific textures.
Revised: RoboTHOR [13]houses use wall panels and floors with very specific textures.

Original: Zero-shot transfer results.
Revised: Zero-shot transfer results.

Original: Models trained only on P ROC THOR and evaluated 0-shot outperformprevious SoTA models on 3 benchmarks (see Table 2).
Revised: Models trained only on P ROC THOR and evaluated zero-shot outperformprevious SoTA models on 3 benchmarks (refer to zero-shot rows of Table 2).

Original: These strong results suggest that modelsgeneralize to not only unseen objects and scenes, but also new appearance and layout statistics.
Revised: These are very strongresults since the models generalize to not only unseen objects and scenes, but also different appearanceand layout statistics.

Original: Fine-tuning results.
Revised: Fine-tuning results.

Original: Further fine-tuning of the model using each benchmark’s training data, achievesstate-of-the-art results on all benchmarks (refer to fine-tune rows of Table 2).
Revised: Further fine-tuning of the model using each benchmark’s training data, achievesstate-of-the-art results on all benchmarks (refer to fine-tune rows of Table 2).

Original: Notably, our model isranked first on three public leaderboards as of 10am PT, June 14th 2022:
Revised: Notably, our model isranked first on three public leaderboards at the time of submission: Habitat 2022 ObjectNav challenge,

Original: Habitat 2022 ObjectNavchallenge, AI2-THOR Rearrangement 2022 challenge, and RoboTHOR ObjectNav challenge.
Revised: AI2-THOR Rearrangement 2022 challenge, and RoboTHOR ObjectNav challenge.

Original: Itshould be noted that our model achieves these results using a very simple architecture and only RGBimages.
Revised: It should benoted that our model achieves these results using a very simple architecture and only RGB images.

Original: Other techniques typically use more complex architectures that include mapping or visualodometry modules and use additional perception sensors such as depth images.
Revised: Other techniques typically use more complex architectures that include mapping or visual odometrymodules and use additional perception sensors such as depth images.

Original: Scale ablation.
Revised: Scale ablation.

Original: To evaluate the effect of scale we train the models on 10, 100, 1k, and 10k houses.
Revised: To evaluate the effect of scale we train the models on 10, 100, 1,000, and 10,000houses.

Revised: For this experiment

Original: Here, we do not use any material augmentations.
Revised: [Table] we do not use any material augmentations.

Original: As shown in Table 3, the performance improves aswe use more houses for training, demonstrating the benefits of large-scale data for E-AI tasks.
Revised: As shown in Table 3, theperformance improves as we use more houses for training, demonstrating the benefits of large-scaledata for Embodied AI tasks

Revised: [Table]

Original: [Table]
Revised: [Table]

Original:  and evaluated 0-shot and with fine-tuning on several E-AI benchmarks.
Revised: models trained on ProcTHOR and evaluated 0-shot and with fine-tuning on several E-AI benchmarks.

Original: For each benchmark we also compare to the relevant baselines (previous
Revised: For each benchmark we also compare to the relevant baselines (previous

Original: SoTA or leaderboard submissions where applicable).
Revised: SoTA or leaderboard submissions where applicable).

Original: a EmbCLIP [19] trained on R OBO THOR, b
Revised: a EmbCLIP [23] trained on R OBO THOR, b

Original: EmbCLIP [19] trained on AI2-iTHOR, c submission on the Habitat 2022 ObjectNav leaderboard [27].
Revised: EmbCLIP [23] trained on AI2-iTHOR, c submission on the Habitat 2022 ObjectNav leaderboard [1].

Original: d

Original: For HM3D we present results when pretraining using the EmbCLIP architecture (which uses CLIPpretrained ResNet50) as well as with a “Large” model which uses a larger CLIP backbone CNN as well as a wider RNN, see supplement for details.

Original: e uses the model from [12] but retrains on the complete iTHOR data with RGB inputs.
Revised: d uses the model from [17] but retrains on the complete iTHOR data with RGB inputs.

Original: 0-shot results, whereby models are pre-trained on P ROC THOR-10K and do not use any training data from the benchmark that they are evaluated on.
Revised: 0-shot results, whereby models are pre-trained on P ROC THOR-10K and do not use any training data from the benchmark that they are evaluated on.

Original: [Table]
Revised: [Table]

Original:  Conclusion
Revised:  Conclusion

Original: We propose P ROC THOR to procedurally generate arbitrarily large sets of interactive, physics- enabled houses for Embodied AI research.
Revised: We propose P ROC THOR, a framework to procedurally generate arbitrarily large sets of interactive,physics-enabled houses for Embodied AI research.

Original: We pre-train simple models on 10k generated houses and show SOTA results across 6 embodied tasks with strong 0-shot results
Revised: We pre-train simple models on 10,000 generatedhouses and show state-of-the-art results across 6 embodied navigation and manipulation benchmarkswith strong 0-shot results, even outperforming

Original: Checklist
Revised: Checklist

Original: For all authors...
Revised: For all authors...

Original: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’scontributions and scope?
Revised: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?

Original: [Yes](b) Did you describe the limitations of your work?
Revised: [Yes] (b) Did you describe the limitations of your work?

Original: [Yes] Please refer to the Appendix.(c)
Revised: [Yes] Please refer to the Appendix.

Original:  Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] Pleaserefer to the Appendix.(d)
Revised: [Yes] Pleaserefer to the Appendix.

Original:  Have you read the ethics review guidelines and ensured that your paper conforms tothem?
Revised: (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?

Original: [Yes]2.
Revised: [Yes]

Original: If you are including theoretical results...
Revised: If you are including theoretical results...

Original: (a) Did you state the full set of assumptions of all theoretical results?
Revised: (a) Did you state the full set of assumptions of all theoretical results?

Original: [N/A](b)
Revised: [N/A](b)

Original: Did you include complete proofs of all theoretical results?
Revised: Did you include complete proofs of all theoretical results?

Original: [N/A]
Revised: [N/A]

Original: If you ran experiments...
Revised: If you ran experiments...

Original: (a) Did you include the code, data, and instructions needed to reproduce the main ex-
Revised: (a) Did you include the code, data, and instructions needed to reproduce the main experi-

Original: perimental results (either in the supplemental material or as a URL)?
Revised: mental results (either in the supplemental material or as a URL)?

Original: [Yes] We have
Revised: [No] The presented

Original: provided code in the supplementary material and provided anonymous links in therebuttal.
Revised: environment, P ROC THOR, will be open sourced soon and all code will be made available soon.

Original: (b) Did you specify all the training details (e.g., data splits, hyperparameters,
Revised: (b) Did you specify all the training details (e g., data splits, hyperparameters,

Original: how they
Revised: how they

Original: were chosen)?
Revised: were chosen)?

Original: [Yes] Please refer to the Appendix.
Revised: [Yes] Please refer to the Appendix.

Original: (c) Did you report error bars (e g., with respect to the random seed after running experi-
Revised: (c) Did you report error bars (e.g., with respect to the random seed after running experi-

Original: ments multiple times)?
Revised: ments multiple times)?

Original: [Yes] We observed training to be robust to different random seeds.
Revised: [No] Training was too costly to run multiple runs.

Original: See the Robustness section of the appendix for details.

Original: (d) Did you include the total amount of compute and the type of resources used (e
Revised: (d) Did you include the total amount of compute and the type of resources used (e

Original: g., type
Revised: g., type

Original: of GPUs, internal cluster, or cloud provider)?
Revised: of GPUs, internal cluster or cloud provider)?

Original: [Yes] Please refer to the Appendix.
Revised: [Yes] Please refer to the Appendix.

Original: If you are using existing assets (e
Revised: If you are using existing assets (e

Original: g., code, data, models) or curating/releasing new assets...
Revised: g , code, data, models) or curating/releasing new assets...

Original: (a) If your work uses existing assets, did you cite the creators?
Revised: (a) If your work uses existing assets, did you cite the creators?

Original: [Yes]
Revised: [Yes]

Original: (b) Did you mention the license of the assets?
Revised: (b) Did you mention the license of the assets?

Original: [Yes] Please refer to the Appendix.
Revised: [Yes] Please refer to the Appendix.

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: c) Did you include any new assets either in the supplemental material or as a URL?

Original: [Yes]
Revised: [No]

Original: The environment including all the assets is included.
Revised: The environment including all the assets will be released publicly soon.

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re 
Revised: [Table] Did you discuss whether and how consent was obtained from people whose data you’re [Table]

Original: using/curating?
Revised: using/curating?

Original: [N/A]
Revised: [N/A]

Original: (e) Did you discuss whether the data you are using/curating contains personally identifiable 
Revised: [Table] Did you discuss whether the data you are using/curating contains personally identifiable [Table]

Original: information or offensive content?
Revised: information or offensive content

Original: [N/A]
Revised: [N [Table]

Original: If youused crowdsourcing or conducted research with human subjects...
Revised: you crowdsourcing or conducted research with human subjects [Table]

Original: (a) Did you include thefull text of instructions givento participants and screenshots, if
Revised: include the text of instructions given participants and screenshots, [Table]

Revised: Review

Revised: [Table]

Revised: you

Original: applicable?
Revised: estimated paid to participants the amount

Original: [N/A]
Revised: [Table]

Revised: spent on participant compensation