Original: L EARNING TO M AP FOR A CTIVE S EMANTIC G OAL N AVIGATION
Revised: L EARNING TO M AP FOR A CTIVE S EMANTIC G OAL N AVIGATION

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: We consider the problem of object goal navigation in unseen environments.
Revised: We consider the problem of object goal navigation in unseen environments.

Original: Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments.
Revised: Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments.

Original: Current methods learn to implicitly encode these priors through goal-oriented navigation policy functions operating on spatial representations that are limited to the agent’s observable areas.
Revised: Current methods learn to implicitly encode these priors through goal-oriented navigation policy functions operating on spatial representations that are limited to the agent’s observable areas.

Original: In this work, we propose a novel framework that actively learns to generate semantic maps outside the ﬁeld of view of the agent and leverages the uncertainty over the semantic classes in the unobserved areas to decide on long term goals.
Revised: In this work, we propose a novel framework that actively learns to generate semantic maps outside the ﬁeld of view of the agent and leverages the uncertainty over the semantic classes in the unobserved areas to decide on long term goals.

Original: We demonstrate that through this spatial prediction strategy, we are able to learn semantic priors in scenes that can be leveraged in unknown environments.
Revised: We demonstrate that through this spatial prediction strategy, we are able to learn semantic priors in scenes that can be leveraged in unknown environments.

Original: Additionally, we show how different objectives can be deﬁned by balancing exploration with exploitation during searching for semantic targets.
Revised: Additionally, we show how different objectives can be deﬁned by balancing exploration with exploitation during searching for semantic targets.

Original: Our method is validated in the visually realistic environments of the Matterport3D dataset and show improved results on object goal navigation over competitive baselines.
Revised: Our method is validated in the visually realistic environments of the Matterport3D dataset and show improved results on object goal navigation over competitive baselines.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: What enables biological systems to successfully navigate to semantic targets in novel environments?
Revised: What enables biological systems to successfully navigate to semantic targets in novel environments?

Original: Consider the example of a dog whose food tray at its own house is situated next to the fridge.
Revised: Consider the example of a dog whose food tray at its own house is situated next to the fridge.

Original: Upon entering a new house for the ﬁrst time, the dog will look for its food tray next to the fridge, even though the new house can largely differ in appearance and layout.
Revised: Upon entering a new house for the ﬁrst time, the dog will look for its food tray next to the fridge, even though the new house can largely differ in appearance and layout.

Original: This is remarkable, as it suggests that the dog is able to encode spatial associations between semantic entities that can be leveraged when trying to accomplish a navigation task.
Revised: This is remarkable, as it suggests that the dog is able to encode spatial associations between semantic entities that can be leveraged when trying to accomplish a navigation task.

Original: Humans exhibit the same skills in similar scenarios, albeit more nuanced, since given existing observations we can consciously choose to trust our prior knowledge over the semantic structure of the world, or to continue exploring the environment.
Revised: Humans exhibit the same skills in similar scenarios, albeit more nuanced, since given existing observations we can consciously choose to trust our prior knowledge over the semantic structure of the world, or to continue exploring the environment.

Original: In other words, if we have a partial view of a room containing an oven, we can infer that a fridge most likely exists in the unobserved space.
Revised: In other words, if we have a partial view of a room containing an oven, we can infer that a fridge most likely exists in the unobserved space.

Original: In addition, if we are trying to reach the sofa, then we can infer with high certainty that it will be located in a different room.
Revised: In addition, if we are trying to reach the sofa, then we can infer with high certainty that it will be located in a different room.

Original: This implies that we have internal mechanisms for quantifying the uncertainty of inferred information from unobserved spaces, which guides our decision making process.
Revised: This implies that we have internal mechanisms for quantifying the uncertainty of inferred information from unobserved spaces, which guides our decision making process.

Original: Inspired by these observations, in this work, we study the problem of object goal navigation for robotic agents in unseen environments and propose an active learning method for encoding semantic priors in indoor scenes.
Revised: Inspired by these observations, in this work, we study the problem of object goal navigation for robotic agents in unseen environments and propose an active learning method for encoding semantic priors in indoor scenes.

Original: Our approach involves learning a mapping model than can predict (hallucinate) semantics in unobserved regions of the map containing both objects (e.g. chairs, beds) and structures (e.g. ﬂoor, wall), and during testing uses the uncertainty over these predictions to plan a path towards the target.
Revised: Our approach involves learning a mapping model than can predict (hallucinate) semantics in unobserved regions of the map containing both objects (e.g. chairs, beds) and structures (e.g. ﬂoor, wall), and during testing uses the uncertainty over these predictions to plan a path towards the target.

Original: Contrary to traditional approaches for mapping and navigation Cadena et al. (i.e. SLAM) where the focus is on building accurate 3D metric maps, our uncertainty formulation is designed to capture our lack of conﬁdence about whether a certain object exists at a particular location.
Revised: Contrary to traditional approaches for mapping and navigation (Cadena et al., 2016) (i.e. SLAM) where the focus is on building accurate 3D metric maps, our uncertainty formulation is designed to capture our lack of conﬁdence about whether a certain object exists at a particular location.

Original: This results in a much more meaningful representation, suitable for target-driven tasks.
Revised: This results in a much more meaningful representation, suitable for target-driven tasks.

Original: Recently, learned approaches to navigation have been gaining popularity, where initial efforts in addressing target-driven navigation focused on end-to-end reactive approaches that learn to map pixels directly to actions Zhu et al.
Revised: Recently, learned approaches to navigation have been gaining popularity, where initial efforts in addressing target-driven navigation focused on end-to-end reactive approaches that learn to map pixels directly to actions (Zhu et al., 2017; Mousavian et al., 2019). These methods do not have an a r

Original: (2017); Mousavian et al.
Revised: X i v : s ub m it/ [ c s . C V ]

Original: These methods do not have an explicit representation of the environment and tend to suffer from poor generalization.
Revised: M a r explicit representation of the environment and tend to suffer from poor generalization.

Original: To remedy this issue, most current methods learn a map representation that enables the encoding of prior information about the geometry and semantics of a scene, acting as an episodic memory Chaplot et al.
Revised: To remedy this issue, most current methods learn a map representation that enables the encoding of prior information about the geometry and semantics of a scene, acting as an episodic memory (Chaplot et al., 2020b;a;

Original: (2020b;a); Gupta et al. ; Georgakis et al.
Revised: Gupta et al., 2017; Georgakis et al., 2019).

Original: However, maps created by these methods are restricted to contain information only from areas that the agent has directly observed, which led to the introduction of spatial prediction models that either anticipate occupancy  Ramakrishnan et al.
Revised: However, maps created by these methods are restricted to contain information only from areas that the agent has directly observed, which led to the introduction of spatial prediction models that either anticipate occupancy (Santhosh Kumar Ramakrishnan &

Original: (2020) or room layouts Narasimhan et al. (2020) beyond the agent’s ﬁeld of view and demonstrated improved performance on navigation tasks.
Revised: Grauman, 2020) or room layouts (Narasimhan et al., 2020) beyond the agent’s ﬁeld of view and demonstrated improved performance on navigation tasks.

Original: Our work differs from these methods in three principled ways: 1) We formulate an active training strategy for learning the semantic maps, 2) we exploit the uncertainty over the predictions in the planning process, and 3) in contrast to predicting occupancy, our model tackles a harder problem which requires learning semantic patterns (e.g. tablesare usually surrounded by chairs).
Revised: Our work differs from these methods in three principled ways: 1) We formulate an active training strategy for learning the semantic maps, 2) we exploit the uncertainty over the predictions in the planning process, and 3) in contrast to predicting occupancy, our model tackles a harder problem which requires learning semantic patterns (e.g. tables surrounded by chairs).

Original: In this work we introduce Learning to Map (L2M), a novel framework for object-goal navigation consisting of two parts.
Revised: In this work we introduce Learning to Map (L2M), a novel framework for object-goal navigation consisting of two parts.

Original: First, we actively learn an ensemble of two-stage segmentation models by choosing training samples through an information gain objective.
Revised: First, we actively learn an ensemble of two-stage segmentation models by choosing training samples through an information gain objective.

Original: The models operate on top-down maps and predict both occupancy and semantic regions.
Revised: The models operate on top-down maps and predict both occupancy and semantic regions.

Original: Second, we estimate the model uncertainty through the disagreement formulation of ensemble models  from Pathak et al. 
Revised: Second, we estimate the model uncertainty through the disagreement between models in an ensemble from Pathak et al. Seung et al.

Original: (2019), and show its effectiveness in deﬁning objectives in planners to actively select long-term goals for semantic navigation.
Revised: (1992), and show its effectiveness in deﬁning objectives in planners to actively select long-term goals for semantic navigation.

Original: In addition, we investigate different information gain objectives during active training and illustrate how the use of model uncertainty can balance exploration with exploitation in ﬁnding semantic targets.
Revised: In addition, we investigate different information gain objectives during active training and illustrate how the use of model uncertainty can balance exploration with exploitation in ﬁnding semantic targets.

Original: Our proposed approach demonstrates improved success rates on the object-goal navigation task over competitive baselines in the Matterport3D Chang et al.
Revised: Our proposed approach demonstrates improved success rates on the object-goal navigation task over competitive baselines in the Matterport3D (Chang et al., 2017)

Original: (2017) dataset using the Habitat Savva et al. (2019) simulator.
Revised: dataset using the Habitat (Savva et al., 2019) simulator.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Semantic SLAM.
Revised: Semantic SLAM.

Original: Classical approaches for navigation focus on building 3D representations of the environment before considering downstream tasks Cadena et al.
Revised: Classical approaches for navigation focus on building 3D representations of the environment before considering downstream tasks (Cadena et al., 2016).

Original: While these methods are typically geometric, several SLAM methods have attempted to associate semantic information to the reconstructed geometric map, mainly at the object-level Salas-Moreno et al. ; Yang & Scherer (2019); McCormac et al.
Revised: While these methods are typically geometric, several SLAM methods have attempted to associate semantic information to the reconstructed geometric map, mainly at the object-level (Salas-Moreno et al., 2013; Yang & Scherer, 2019;

Original: ; Bowman et al. (2017); Kostavelis & Gasteratos (2015).
Revised: McCormac et al., 2018; Bowman et al., 2017; Kostavelis & Gasteratos, 2015).

Original: For example, in McCormac et al.
Revised: For example, in McCormac et al.

Original: (2018) instance segmentations predicted by Mask R-CNN are incorporated to facilitate per-object reconstructions, while the work of Bowman et al.
Revised: (2018) instance segmentations predicted by Mask R-CNN are incorporated to facilitate per-object reconstructions, while the work of Bowman et al.

Original: (2017) proposes a probabilistic formulation to address uncertain object data association.
Revised: (2017) proposes a probabilistic formulation to address uncertain object data association.

Original: However, SLAM systems rarely consider active exploration as they are not naturally compatible with task-driven learnable representations from deep learning architectures that can encode semantic information.
Revised: However, SLAM systems rarely consider active exploration as they are not naturally compatible with task-driven learnable representations from deep learning architectures that can encode semantic information.

Original: Other recent works Katsumata et al. (2020); Cartillier et al. (2020) have sought to build 2D semantic maps and focused either on semantic transfer of a global scene or assumed the environments were accessible before-hand.
Revised: Other recent works (Katsumata et al., 2020; Cartillier et al., 2021) have sought to build 2D semantic maps and focused either on semantic transfer of a global scene or assumed the environments were accessible before-hand.

Original: In contrast, our proposed approach tackles the object goal task in unknown environments by actively learning how to predict semantics in both observed and unobserved areas of the map around the agent.
Revised: In contrast, our proposed approach tackles the object goal task in unknown environments by actively learning how to predict semantics in both observed and unobserved areas of the map around the agent.

Original: Learning based navigation methods.
Revised: Learning based navigation methods.

Original: There has been a recent surge of learning based methods Zhu et al. (2017); Mousavian et al. ; Gupta et al. ; Chen et al.
Revised: There has been a recent surge of learning based methods (Zhu et al., 2017; Mousavian et al., 2019; Gupta et al., 2017; Chen et al., 2020; Chaplot et al., 2020b;

Original: ; Chaplot et al.

Original: (2020b);

Original: Fang et al. ; Yang et al. ; Ye et al. ; Zhang et al.
Revised: Fang et al., 2019; Yang et al., 2019;

Original: ; Chattopadhyay et al.

Original: (2021) for indoor navigation tasks Anderson et al. ; Batra et al. Das et al. (2018), propelled by the introduction of high quality simulators such as Gibson Xia et al.
Revised: Ye et al., 2021; Zhang et al., 2021; Chattopadhyay et al., 2021) for indoor navigation tasks (Anderson et al., 2018; Batra et al., 2020; Das et al., 2018), propelled by the introduction of high quality simulators such as Gibson (Xia et al., 2018), Habitat (Savva et al., 2019), and AI2-THOR (Kolve et al., 2017).

Original: (2018), Habitat Savva et al.

Original: (2019), and

Original: AI2-THOR Kolve et al.

Original: Methods which use explicit task-dependent map representations Parisotto & Salakhutdinov (2017); Gupta et al. (2017); Chaplot et al. (2020b;a);
Revised: Methods which use explicit task-dependent map representations (Parisotto & Salakhutdinov, 2018; Gupta et al., 2017; Chaplot et al., 2020b;a;

Original: Georgakis et al. ; Gordon et al. ; Mishkin et al. (2019) have shown to generalize better in unknown environments than end-to-end approaches with implicit world representations.
Revised: Georgakis et al., 2019; Gordon et al., 2018; Mishkin et al., 2019) have shown to generalize better in unknown environments than end-to-end approaches with implicit world representations.

Original: For example, in Gupta et al.
Revised: For example, in Gupta et al.

Original: (2017) a differentiable mapper learns to predict top-down egocentric views of the scene from RGB images, followed by a differentiable planner, while in Chaplot et al.
Revised: (2017) a differentiable mapper learns to predict top-down egocentric views of the scene from RGB images, followed by a differentiable planner, while in Chaplot et al.

Original: (2020a)
Revised: (2020a)

Original: Mask R-CNN is used to build a top-down semantic map of the scene used by a learned policy that predicts goal locations in the map.
Revised: Mask R-CNN is used to build a top-down semantic map of the scene used by a learned policy that predicts goal locations in the map.

Original: More conceptually similar to our method, are approaches that attempt to encode semantic or layout priors by learning to predict outside the ﬁeld-of-view of the agent Ramakrishnan et al.
Revised: More conceptually similar to our method, are approaches that attempt to encode semantic or layout priors by learning to predict outside the ﬁeld-of-view of the agent (Santhosh

Original: ; Liang et al. Narasimhan et al.
Revised: Kumar Ramakrishnan & Grauman, 2020; Liang et al., 2021; Narasimhan et al., 2020; Georgakis et al., 2022).

Original: In contrast to all these works we formulate an active, target-independent strategy to predict semantic maps and deﬁne goal selection objectives.
Revised: In contrast to all these works we formulate an active, target-independent strategy to predict semantic maps and deﬁne goal selection objectives.

Original: Uncertainty Estimation.
Revised: Uncertainty Estimation.

Original: Many recent works estimate uncertainty of deep learning models Gal (2016); Abdar et al.
Revised: Many recent works estimate uncertainty of deep learning models (Gal, 2016; Abdar et al., 2021).

Original: We leverage the approach ﬁrst proposed by Seung et al.
Revised: In some cases, these uncertainty estimates can be used as objectives for active exploration (Sekar et al., 2020; Pathak et al., 2019) since maximizing epistemic uncertainty is used as a proxy for maximizing information gain (Seung et al., 1992; Pathak et al., 2019). Uncertainty estimates of deep learning models able to be used as objectives for active exploration fall into two categories: Bayesian neural networks and ensembles. Lakshminarayanan et al. Gawlikowski et al.

Original: (1992) to estimate our model (epistemic) uncertainty with the variance between the output of an ensemble of models.
Revised: (2021) found the multiple passes required by Bayesian methods for every prediction can result in a higher computational cost than small ensembles.

Original: This was ﬁrst used for active exploration in vision-based reinforcement learning by Pathak et al.
Revised: In addition, ensembles are simpler to implement with little hyperparameter tuning in comparison to Bayesian methods, leading us to focus our work on ensemble based approaches.

Original: Maximizing epistemic uncertainty is used as a proxy for maximizing information gain Seung et al.
Revised: We tested estimating epistemic uncertainty with entropy (Shannon, 1948), an approximation of model information gain using predictive entropy (BALD) (Houlsby et al., 2011), and variance between model outputs (Seung et al., 1992).

Original: ; Pathak et al. We use this uncertainty objective to actively ﬁne-tune our models with a training procedure  similar to the active learning approaches leveraged during training presented by Bucher et al.
Revised: We found maximizing the variance yielded the best performance as an objective to actively ﬁne-tune our map prediction models. This training procedure is similar to the active learning approaches leveraged during training presented by Bucher et al.

Original: (2020); Chaplot et al.
Revised: (2021); Chaplot et al.

Original: (2020c)
Revised: (2020c)

Original: ; Sener & Savarese (2017).
Revised: ; Sener & Savarese (2018).

Original: In particular,

Original: Chaplot et al.

Original: (2020c) uses this active training method to improve performance on a semantic segmentation model, leading us to initially hypothesize analogous results would be possible for our semantic hallucination task.

Original: We also use this epistemic uncertainty estimate to construct conﬁdence bounds for our estimated probability distribution which we use to select goals for target-driven navigation at test time.
Revised: We also use our epistemic uncertainty estimate to construct conﬁdence bounds for our estimated probability distribution which we use to select goals for target-driven navigation at test time.

Original: Both lower Galichet et al. and upper Auer et al. (2002) conﬁdence bound strategies for balancing exploration, exploitation, and safety have been previously proposed in the multi-armed bandit literature and extended for use inMDPs Azar et al.
Revised: Both lower (Galichet et al., 2013) and upper (Auer et al., 2002) conﬁdence bound strategies for balancing exploration, exploitation, and safety have been previously proposed in the multi-armed bandit literature and extended for use in

Original:  and reinforcement learning Chen et al.
Revised: MDPs (Azar et al., 2017) and reinforcement learning (Chen et al., 2017).

Original:  A PPROACH
Revised:  A PPROACH

Original: We present a new framework for object-goal navigation that uses a learned semantic map predictor to select informative goals.
Revised: We present a new framework for object-goal navigation that uses a learned semantic map predictor to select informative goals.

Original: In contrast to prior work  Ramakrishnan et al. (2020); Chaplot et al. (2020a), we leverage the predictions outside the ﬁeld-of-view of the agent to formulate uncertainty-based goal selection policies.
Revised: In contrast to prior work (Santhosh Kumar Ramakrishnan & Grauman, 2020; Chaplot et al., 2020a), we leverage the predictions outside the ﬁeld-of-view of the agent to formulate uncertainty-based goal selection policies.

Original: Furthermore, we actively collect data for training the map predictor and investigate different information gain objectives.
Revised: Furthermore, we actively collect data for training the map predictor and investigate different information gain objectives.

Original: Due to our goal selection policy formulation, our method does not need to be trained speciﬁcally to predict goals for every target object, enabling a target-independent learning of the semantic priors.
Revised: Due to our goal selection policy formulation, our method does not need to be trained speciﬁcally to predict goals for every target object, enabling a target-independent learning of the semantic priors.

Original: Our method takes as input an RGB-D observation, and predicts semantics in the unobserved areas of the map.
Revised: Our method takes as input an RGB-D observation, and predicts semantics in the unobserved areas of the map.

Original: This is followed by goal selection based on the estimated uncertainty of the predictions.
Revised: This is followed by goal selection based on the estimated uncertainty of the predictions.

Original: Finally a local policy is responsible for reaching the goal.
Revised: Finally a local policy is responsible for reaching the goal.

Original: An overview of our pipeline can be seen in Figure 1.
Revised: An overview of our pipeline can be seen in Figure 1.

Original: 3. S EMANTIC M AP P REDICTION
Revised: 3. S EMANTIC M AP P REDICTION

Original: We describe a method for learning how to map by predicting the semantic information outside the ﬁeld of view of the agent.
Revised: We describe a method for learning how to map by predicting the semantic information outside the ﬁeld of view of the agent.

Original: We emphasize that this goes beyond traditional mapping (i.e. accumulating multiple views in an agent’s path) as it relies on prior information encoded as spatial associations between semantic entities in order to hallucinate the missing information.
Revised: We emphasize that this goes beyond traditional mapping (i.e. accumulating multiple views in an agent’s path) as it relies on prior information encoded as spatial associations between semantic entities in order to hallucinate the missing information.

Original: Motivated by the past success of semantic segmentation models in learning contextual information Zhang et al.
Revised: Motivated by the past success of semantic segmentation models in learning contextual information (Zhang et al., 2018;

Original: Yuan et al. (2019), we formulate the semantic map prediction as a two-stage segmentation problem.
Revised: Yuan et al., 2020), we formulate the semantic map prediction as a two-stage segmentation problem.

Original: Our method takes as input an incomplete occupancy region p t ∈ R | C o |× h × w and a ground-projected semantic segmentation

Original: ˆ s t ∈ R | C s |× h × w at time-step t .

Original: The output is a top-down semantic local region ˆ m t ∈ R | C s |× h × w , where C o is the set of occupancy classes containing unknown , occupied , and free , C s is the set of semantic object classes, and h , w are the dimensions of the local crop.

Original: To obtain p t we use the provided camera intrinsics and depth observation at time t to ﬁrst get a point cloud which is then discretized and ground-projected similar to Ramakrishnan et al.

Original: To estimate ˆ s t we ﬁrst train a UNet Ronneberger et al.

Original: (2015) model to predict the semantic segmentation of the RGB observation at time t .

Original: All local regions are egocentric, i.e., the agent is in the middle of the crop looking upwards.

Original: Each spatial location in our map (cell) has dimensions 10 cm × 10 cm .

Original: The proposed two-stage segmentation model predicts the hallucinated semantic region in two stages.
Revised: The proposed two-stage segmentation model predicts the hallucinated semantic region in two stages.

Original: First, we estimate the missing values for the occupancy crop in the unobserved areas by learning to hallucinate unseen spatial conﬁgurations based on what is already observed.
Revised: First, we estimate the missing values for the occupancy crop in the unobserved areas by learning to hallucinate unseen spatial conﬁgurations based on what is already observed.

Original: Second, given predicted occupancy, we predict the ﬁnal semantic region ˆ m t .
Revised: Second, given predicted occupancy, we predict the ﬁnal semantic region ˆ m t .

Original: These steps are realized as two
Revised: These steps are realized as two

Original: UNet encoder-decoder models, f o that predicts in occupancy space ˆ p t = f
Revised: UNet encoder-decoder models, f o that predicts in occupancy space ˆ p t = f

Original: o ( p t ; θ o ) , and f s that predicts in semantic space
Revised: o ( p t ; θ o ) , and f s that predicts in semantic space

Original: ˆ m t = f s (ˆ p t ⊕ ˆ s t ; θ s ) , where ˆ p t is the predicted local occupancy crop which includes unobserved regions, ⊕ refers to the concatenation operation, and θ o , θ s are the randomly initialized weights of the occupancy and semantic networks respectively.
Revised: ˆ m t = f s (ˆ p t ⊕ ˆ s t ; θ s ) , where ˆ p t is the predicted local occupancy crop which includes unobserved regions, ⊕ refers to the concatenation operation, and θ o , θ s are the randomly initialized weights of the occupancy and semantic networks respectively.

Original: The image segmentation model is trained independently and its ground projected output ˆ s t conditions f s on the egocentric single-view observation of the agent.
Revised: The image segmentation model is trained independently and its ground projected output ˆ s t conditions f s on the egocentric single-view observation of the agent.

Original: The model is trained end-to-end using pixel-wise cross-entropy losses for both occupancy and semantic classes and predicts a probability distribution over the objects for each map location.
Revised: The model is trained end-to-end using pixel-wise cross-entropy losses for both occupancy and semantic classes and predicts a probability distribution over the classes for each map location.

Original: We assume that ground-truth semantic information is available such that we can generate egocentric top-down occupancy and semantic examples.
Revised: We assume that ground-truth semantic information is available such that we can generate egocentric top-down occupancy and semantic examples.

Original: This combined objective incentivizes learning to predict plausible semantic regions by having the semantic loss backpropagating gradients affecting both f o and f s .
Revised: This combined objective incentivizes learning to predict plausible semantic regions by having the semantic loss backpropagating gradients affecting both f o and f s .

Original: Also, performing this procedure enables the initial hallucination of unknown areas over a small set of classes C o , before expanding to the more difﬁcult task of predicting semanticobject categories C s .
Revised: Also, performing this procedure enables the initial hallucination of unknown areas over a small set of classes C o , before expanding to the more difﬁcult task of predicting semantic categories C s which includes predicting the placement of objects together with scene structures such as walls in the map.

Original: An overview of the semantic map predictor can be seen in Figure 2.
Revised: An overview of the semantic map predictor can be seen in Figure 2.

Original: During a navigation episode, the local semantic region is registered to a global map which is used during planning.
Revised: During a navigation episode, the local semantic region is registered to a global map which is used during planning.

Original: Since we predict a probability distribution at each location over the set of objects, the local regions are registered using Bayes Theorem.
Revised: Since we predict a probability distribution at each location over the set of classes, the local regions are registered using Bayes Theorem.

Original: The global map is initialized with a uniform prior probability distribution across all classes.
Revised: The global map is initialized with a uniform prior probability distribution across all classes.

Original: 3. U NCERTAINTY AS AN O BJECTIVE
Revised: 3. U NCERTAINTY AS AN O BJECTIVE

Original: A key component of a robotic system is its capacity to model what it does not know.
Revised: A key component of a robotic system is its capacity to model what it does not know.

Original: This ability enables an agent to identify failure cases and make decisions about whether to trust its predictions or continue exploring.
Revised: This ability enables an agent to identify failure cases and make decisions about whether to trust its predictions or continue exploring.

Original: In our semantic map prediction problem, estimating the uncertainty over the semantic predictions at each location of the map enables understanding what the model does not know.
Revised: In our semantic map prediction problem, estimating the uncertainty over the semantic predictions at each location of the map enables understanding what the model does not know.

Original: We considered two types of uncertainty we face in modeling vision problems with deep learning: aleatoric and epistemic uncertainty Kendall & Gal (2017); Gal (2016).
Revised: We considered two types of uncertainty we face in modeling vision problems with deep learning: aleatoric and epistemic uncertainty (Kendall & Gal, 2017; Gal, 2016).

Original: First, aleatoric uncertainty is theuncertainty in the system we are modeling.
Revised: First, aleatoric uncertainty is the system uncertainty.

Original: Suppose the true probability of a sofa being at a speciﬁc location given an observation is 30%.
Revised: Suppose the true probability of a sofa being at a speciﬁc location given an observation is 30%.

Original: Consider a scenario where our target is sofa and our model estimates the true probability of 30% that a sofa is at the speciﬁc location.
Revised: Consider a scenario where our target is sofa and our model estimates the true probability of 30% that a sofa is at the speciﬁc location.

Original: Our model would be correct regardless of whether the sofa is present at that location.
Revised: Our model would be correct regardless of whether the sofa is present at that location.

Original: This uncertainty is reﬂected in the probability output of our model.
Revised: This uncertainty is reﬂected in the probability output of our model.

Original: We denote this model f : ( p t , ˆ s t ; θ )
Revised: We denote this model f : ( p t , ˆ s t ; θ )

Original: (cid:55)→ ˆ m t where θ are the parameters of f .
Revised: (cid:55)→ ˆ m t where θ are the parameters of f .

Original: Second, epistemic uncertainty captures the uncertainty over the model’s parameters.
Revised: Second, epistemic uncertainty captures the uncertainty over the model’s parameters.

Original: In training, our objective is to improve the prediction model by identifying cases it underperforms.
Revised: In training, our objective is to improve the prediction model by identifying cases it underperforms.

Original: We use epistemic uncertainty to formulate this objective, as samples with high epistemic uncertainty are associated with increased information gain.
Revised: We use epistemic uncertainty to formulate this objective, as samples with high epistemic uncertainty are associated with increased information gain.

Original: We recall that f is a classiﬁer trained with the cross-entropy loss, so the output of f is a probability distribution.
Revised: We recall that f is a classiﬁer trained with the crossentropy loss, so the output of f is a probability distribution.

Original: In order to estimate epistemic uncertainty, we consider the probabilistic interpretation P ( m t | p t , ˆ s t , θ ) of our model f which deﬁnes a likelihood function over the parameters θ .
Revised: In order to estimate epistemic uncertainty, we consider the probabilistic interpretation P ( m t | p t , ˆ s t , θ ) of our model f which deﬁnes a likelihood function over the parameters θ .

Original: The parameters θ are random variables sampled from the distribution q ( θ ) .
Revised: The parameters θ are random variables sampled from the distribution q ( θ ) .

Original: We construct f as an ensemble of two-stage segmentation models deﬁned over the parameters { θ 1 , ..., θ N } .
Revised: We construct f as an ensemble of two-stage segmentation models deﬁned over the parameters { θ 1 , ..., θ N } .

Revised: Variance between models in the ensemble comes from different random weight initializations in each network (Pathak et al., 2019; Sekar et al., 2020).

Original: Our model estimates the true probability distribution P ( m t | p t , ˆ s t ) by averaging over sampled model weights,P ( m t | p t , ˆ s t )
Revised: Our model estimates the true probability distribution P ( m t | p t , ˆ s t ) by averaging over sampled model weights,

Original:  ≈ E θ ∼ q ( θ ) f
Revised: P ( m t | p t , ˆ s t ) ≈ E θ ∼ q ( θ ) f

Original: ( p t , ˆ s t ; θ )
Revised: ( p t , ˆ s t ; θ )

Original: ≈ 1 N (cid:80)
Revised: ≈ 1 N (cid:80)

Original: Ni =1 f ( p t , ˆ s t ; θ i )
Revised: Ni =1 f ( p t , ˆ s t ; θ i )

Original: Lakshminarayanan et al. ; Gal et al.
Revised: (Lakshminarayanan et al., 2017; Gal et al., 2017).

Original: Then, following prior work Seung et al. ; Pathak et al. (2019), the epistemic uncertainty can be approximated from the variance between the outputs of the models in the ensemble, Var f( p t , ˆ s t ; θ ) .
Revised: Then, following prior work (Seung et al., 1992; Pathak et al., 2019), the epistemic uncertainty can be approximated from the variance between the outputs of the models in the ensemble, Var f

Revised: ( p t , ˆ s t ; θ ) .

Original: We use uncertainty estimation in two distinct ways in our method.
Revised: We use uncertainty estimation in two distinct ways in our method.

Original: First, during training of the semantic predictor we actively select locations of the map with high information gain (Section 3.2.1).
Revised: First, during training of the semantic predictor we actively select locations of the map with high information gain (Section 3.2.1).

Original: Second, during object-goal navigation we actively choose long-term goals that encourage the agent to explore in search of the target object (Section 3.3).
Revised: Second, during object-goal navigation we actively choose long-term goals that encourage the agent to explore in search of the target object (Section 3.3).

Original: 3.2. A CTIVE T RAINING
Revised: 3.2. A CTIVE T RAINING

Original: A typical procedure for training the semantic map predictor would be to sample observations along the shortest path between two randomly selected locations in a scene.
Revised: A typical procedure for training the semantic map predictor would be to sample observations along the shortest path between two randomly selected locations in a scene.

Original: However, this results in collecting a large amount of observations from repetitive or generally uninteresting areas (e.g. spaces devoid of objects of interest).
Revised: However, this results in collecting a large amount of observations from repetitive or generally uninteresting areas (e.g. spaces devoid of objects of interest).

Original: We use that naive strategy for pre-training (around 900K examples), and formulate a following step where we actively collect training samples using an information gain objective.
Revised: We use that naive strategy for pre-training (around 900K examples), and formulate a following step where we actively collect training samples using an information gain objective.

Original: We choose destinations for which the predictions of our pre-trained models maximize this objective.
Revised: We choose destinations for which the predictions of our pre-trained models maximize this objective.

Original: Since we can interpret our hallucinated map ˆ m t as a prediction { ˆ s t +1 , ..., ˆ s t + T } of the semantic segmentation of future observations over some window T , our greedy objective for information gain allows us to collect data in directions where we expect to make the most informative observations.
Revised: Since we can interpret our hallucinated map ˆ m t as a prediction { ˆ s t +1 , ..., ˆ s t + T } of the semantic segmentation of future observations over some window T , our greedy objective for information gain allows us to collect data in directions where we expect to make the most informative observations.

Original: The agent then moves using a local policy (described in Section 3.3.3).
Revised: The agent then moves using a local policy (described in Section 3.3.3).

Original: The models are then ﬁne-tuned using the collected training examples (around 500K).
Revised: The models are then ﬁne-tuned using the collected training examples (around 500K).

Original: We evaluate which observations will be useful data for training by selecting ( x, y )
Revised: We evaluate which observations will be useful data for training by selecting ( x, y )

Original: -grid locations to maximize an approximation of I ( m t ; θ | p t , ˆ s t ) .
Revised: -grid locations to maximize an approximation of I ( m t ; θ | p t , ˆ s t ) .

Original: I ( m t ; θ | p t , ˆ s t ) denotes the information gain with respect to the map m t from an update of the model parameters θ given the occupancy observation p t and semantic map crop ˆ s t .
Revised: I ( m t ; θ | p t , ˆ s t ) denotes the information gain with respect to the map m t from an update of the model parameters θ given the occupancy observation p t and semantic map crop ˆ s t .

Original: For brevity, we specify a grid location as l j ∈ { l 1 , ..., l k } where k = hw for an h × w map region over which our model f estimates ˆ m t .
Revised: For brevity, we specify a grid location as l j ∈ { l 1 , ..., l k } where k = hw for an h × w map region over which our model f estimates ˆ m t .

Original: We select locations  which have the maximum epistemic uncertainty as a proxy for maximizing information gain Pathak et al. Seung et al.
Revised: We select locations from the map which have the maximum epistemic uncertainty as a proxy for maximizing information gain (Pathak et al., 2019; Seung et al., 1992).

Original: To this end, we deﬁne the average epistemic uncertainty across all classes.
Revised: To this end, we deﬁne the average epistemic uncertainty across all classes.

Original: We select locations l j from the map at time t with the greedy policy
Revised: We select locations l j from the map at time t with the greedy policy

Original: [Equation]
Revised: [Equation]

Original: In practice, these locations are selected from the accumulated uncertainty estimates in the global map.
Revised: In practice, these locations are selected from the accumulated uncertainty estimates in the global map.

Original: Alternatives to our chosen active training strategy include policies maximizing entropy Shannon (1948) or an approximation of model information gain using predictive entropy (BALD) Houlsby et al.
Revised: Alternatives to our chosen active training strategy include policies maximizing entropy (Shannon, 1948) or an approximation of model information gain using predictive entropy (BALD) (Houlsby et al., 2011).

Original: We experimentally compare these alternatives to our approach in Section 4.1.
Revised: We experimentally compare these alternatives to our approach in Section 4.1.

Original: 3. G OAL N AVIGATION P OLICY
Revised: 3. G OAL N AVIGATION P OLICY

Original: We study the problem of target-driven navigation within novel environments, which can be formulated as a partially observable Markov decision process (POMDP) ( S, A, O, P ( s (cid:48) | s, a ) , R ( s, a )) .
Revised: We study the problem of target-driven navigation within novel environments, which can be formulated as a partially observable Markov decision process (POMDP) ( S, A, O, P ( s (cid:48) | s, a ) , R ( s, a )) .

Original: We are interested in deﬁning a policy that outputs goal locations as close as possible to a target class c .
Revised: We are interested in deﬁning a policy that outputs goal locations as close as possible to a target class c .

Original: The state space S consists of the agent’s
Revised: The state space S consists of the agent’s

Original: pose x and the semantic predictions ˆ m t accumulated over time in the global map.
Revised: pose x and the semantic predictions ˆ m t accumulated over time in the global map.

Original: The action space A is comprised of the discrete set of locations h × w over the map.
Revised: The action space A is comprised of the discrete set of locations h × w over the map.

Original: The observation space O are the RGB-D egocentric observations, and P ( s (cid:48) | s, a ) are the transition probabilities.
Revised: The observation space O are the RGB-D egocentric observations, and P ( s (cid:48) | s, a ) are the transition probabilities.

Original: A common reward choice for a supervised policy would be R ( s, a )
Revised: A common reward choice for a supervised policy would be R ( s, a )

Original: =
Revised: =

Original: D ( s, c ) − D
Revised: D ( s, c ) − D

Original: s (cid:48) , c ) which is the distance reduced between the agent and the target, where D ( ., . ) is distance on the shortest path.
Revised: s (cid:48) , c ) which is the distance reduced between the agent and the target, where D ( ., . ) is distance on the shortest path.

Original: However, this results in a target-dependent learned policy, which would require re-training when a novel target is deﬁned.
Revised: However, this results in a target-dependent learned policy, which would require re-training when a novel target is deﬁned.

Original: Therefore, we formulate a policy which accumulates the predicted semantic crops ˆ m t at every time-step, and leverages the predicted class probabilities along with uncertainty estimation over the map locations to select informative goals.
Revised: Therefore, we formulate a policy which accumulates the predicted semantic crops ˆ m t at every time-step, and leverages the predicted class probabilities along with uncertainty estimation over the map locations to select informative goals.

Original: 3.3. U PPER C ONFIDENCE B OUND FOR G OAL S ELECTION
Revised: 3.3. U PPER C ONFIDENCE B OUND FOR G OAL S ELECTION

Original: We now use our uncertainty-driven approach to exploration to explicitly propose an objective for goal selection.
Revised: We now use our uncertainty-driven approach to exploration to explicitly propose an objective for goal selection.

Original: During task execution at test time, f cannot gain information because we do not update the model online with agent observations.
Revised: During task execution at test time, f cannot gain information because we do not update the model online with agent observations.

Original: However, the agent gains information by accumulating observations and successive predictions in the global map.
Revised: However, the agent gains information by accumulating observations and successive predictions in the global map.

Original: We construct a policy in order to select goals from unobserved map locations using this accumulated information.
Revised: We construct a policy in order to select goals from unobserved map locations using this accumulated information.

Original: The idea behind our policy is simple; if the agent is not very conﬁdent of where the target is situated, then it should prioritize exploration, otherwise it should focus more on exploiting its knowledge of candidate goal locations.
Revised: The idea behind our policy is simple; if the agent is not very conﬁdent of where the target is situated, then it should prioritize exploration, otherwise it should focus more on exploiting its knowledge of candidate goal locations.

Original: Since our task is target-driven, we can narrow our information gain objective to reduce uncertainty in areas of the map with the highest uncertainty about the target class.
Revised: Since our task is target-driven, we can narrow our information gain objective to reduce uncertainty in areas of the map with the highest uncertainty about the target class.

Original: We denote f c as the function f which only returns the values for a given target class c .
Revised: We denote f c as the function f which only returns the values for a given target class c .

Original: For class c , our ensemble f c estimates
Revised: For class c , our ensemble f c estimates

Original: P c ( m t | p t , ˆ s t ) , the probability class c is at location i given an observation p t and semantic segmentation
Revised: P c ( m t | p t , ˆ s t ) , the probability class c is at location i given an observation p t and semantic segmentation

Original: ˆ s t for each map location.
Revised: ˆ s t for each map location.

Original: The target class uncertainty at test time is given by the variance over the target class predictions Var f c ( p t , ˆ s t ; θ ) .
Revised: The target class uncertainty at test time is given by the variance over the target class predictions Var f c ( p t , ˆ s t ; θ ) .

Original: We propose selecting goals using the upper conﬁdence bound of our estimate of the true probability
Revised: We propose selecting goals using the upper conﬁdence bound of our estimate of the true probability

Original: P c ( m t | p t , ˆ s t ) in order to select locations with high payoffs but also high potential for our model to gain new information.
Revised: P c ( m t | p t , ˆ s t ) in order to select locations with high payoffs but also high potential for our model to gain new information.

Original: Upper conﬁdence bounds have long been used to balance exploration and exploitation in problems with planning under uncertainty Auer et al. ; Azar et al. ; Chen et al.
Revised: Upper conﬁdence bounds have long been used to balance exploration and exploitation in problems with planning under uncertainty (Auer et al., 2002; Azar et al., 2017; Chen et al., 2017).

Original: We denote σ c ( p t , ˆ s t ) =
Revised: We denote σ c ( p t , ˆ s t ) =

Original: (cid:112)
Revised: (cid:112)

Original: Var f c ( p t ,
Revised: Var f c ( p t ,

Original: ˆ s t ; θ ) as the standard deviation of the target class probability, and we denote µ c ( p t , ˆ s t )
Revised: ˆ s t ; θ ) as the standard deviation of the target class probability, and we denote µ c ( p t , ˆ s t )

Original: = 1 N (cid:80) Ni =1 f c ( p t , ˆ s t ; θ i ) .
Revised: = 1 N (cid:80) Ni =1 f c ( p t , ˆ s t ; θ i ) .

Original: Then, we observe the upper bound
Revised: Then, we observe the upper bound

Original: P c ( m t | p t , ˆ s t ) ≤ µ c ( p t , ˆ s t ) + α 1 σ c ( p t , ˆ s t ) holds with some ﬁxed but unknown probability where αis a constant hyperparameter.
Revised: P c ( m t | p t , ˆ s t ) ≤ µ c ( p t , ˆ s t ) + α 1 σ c ( p t , ˆ s t ) holds with some ﬁxed but unknown probability where αis a constant hyperparameter.

Original: Following Chen et al.
Revised: Following Chen et al.

Original: (2017), we use this upper bound and select goals  with the policy
Revised: (2017), we use this upper bound and select goals from any map region the agent has observed or hallucinated with the policy

Original: [Equation]
Revised: [Equation]

Original: In practice, this is evaluated over our predictions and uncertainty estimations accumulated over time.
Revised: In practice, this is evaluated over our predictions and uncertainty estimations accumulated over time.

Original: 3.3. A LTERNATIVE S TRATEGIES
Revised: 3.3. A LTERNATIVE S TRATEGIES

Original: While we found our proposed upper bound strategy to choose the best candidate goal locations, we hypothesized several alternative policies which yield competitive performance.
Revised: While we found our proposed upper bound strategy to choose the best candidate goal locations, we hypothesized several alternative policies which yield competitive performance.

Original: We consider the following interpretations of our uncertainty and probability estimates for object goal navigation.
Revised: We consider the following interpretations of our uncertainty and probability estimates for object goal navigation.

Original: • High µ c ( p t , ˆ s t ) , Low σ c ( p t , ˆ s t ) : we are fairly certain we found the target object • High µ c ( p t , ˆ s t ) , High σ c ( p t , ˆ s t ) : we are uncertain we found the target object • Low µ c ( p t , ˆ s t ) , High σ c ( p t , ˆ s t ) : we are uncertain we did not ﬁnd
Revised: • High µ c ( p t , ˆ s t ) , Low σ c ( p t , ˆ s t ) : we are fairly certain we found the target object • High µ c ( p t , ˆ s t ) , High σ c ( p t , ˆ s t ) : we are uncertain we found the target object • Low µ c ( p t , ˆ s t ) , High σ c ( p t , ˆ s t ) : we are uncertain we did not ﬁnd

Original: the target object • Low µ c ( p t , ˆ s t ) , Low σ c ( p t , ˆ s t ) : we are fairly certain we did not ﬁnd
Revised: the target object • Low µ c ( p t , ˆ s t ) , Low σ c ( p t , ˆ s t ) : we are fairly certain we did not ﬁnd

Original: the target object
Revised: the target object

Original: From these interpretations we see that our upper bound strategy risks choosing locations with high potential information gain (high variance) over locations where we are fairly certain we found the target object (high probability, low variance).
Revised: From these interpretations we see that our upper bound strategy risks choosing locations with high potential information gain (high variance) over locations where we are fairly certain we found the target object (high probability, low variance).

Original: To consider other exploration and exploitation tradeoffs, we also observe the lower bound P c ( m t | p t , ˆ s t ) ≥ µ c ( p t , ˆ s t ) − α 1 σ c ( p t , ˆ s t ) holds with some ﬁxed but unknown probability.
Revised: To consider other exploration and exploitation tradeoffs, we also observe the lower bound P c ( m t | p t , ˆ s t ) ≥ µ c ( p t , ˆ s t ) − α 1 σ c ( p t , ˆ s t ) holds with some ﬁxed but unknown probability.

Original: Then, we can formulate the following alternative goal selection strategies.
Revised: Then, we can formulate the following alternative goal selection strategies.

Original: Lower Bound Strategy.
Revised: Lower Bound Strategy.

Original: Lower bound strategies optimize safety Galichet et al.
Revised: Lower bound strategies optimize safety (Galichet et al., 2013).

Original: We can differentiate between multiple locations which have high probability of containing our target class by choosing the one with the lowest uncertainty with the objective
Revised: We can differentiate between multiple locations which have high probability of containing our target class by choos-

Original: [Equation]
Revised: [Table]

Revised: ing the one with the lowest uncertainty with the objective: arg max l j ( µ c ( p t , ˆ s t ) − α 1 σ c ( p t , ˆ s t )) .

Original: However, this strategy does not explore regions with high uncertainty to gain information.
Revised: However, this strategy does not explore regions with high uncertainty to gain information.

Original: Mixed Strategy.
Revised: Mixed Strategy.

Original: We can try to balance the pros and cons of the lower and upper bound strategies by switching between the two based on how high the probability is that we found the target object.
Revised: We can try to balance the pros and cons of the lower and upper bound strategies by switching between the two based on how high the probability is that we found the target object.

Original: We tune a hyperparameter α 2 to determine the cutoffs for "high" and "low" values of µ c ( p t , ˆ s t ) .
Revised: We tune a hyperparameter α 2 to determine the cutoffs for "high" and "low" values of µ c ( p t , ˆ s t ) .

Original: We select goals with the objective
Revised: We select goals with the objective: arg max l j ( µ c ( p t , ˆ s t ) + sgn

Original: [Equation] [Table]
Revised: ( α 2 − µ c ( p t , ˆ s t ))

Original:  so that we choose a safe strategy via our lower bounds when the probability of the class at a location is high and an exploration strategy via our upper bounds when the probability of the class is not high.
Revised: α 1 σ c ( p t , ˆ s t )) , so that we choose a safe strategy via our lower bounds when the probability of the class at a location is high and an exploration strategy via our upper bounds when the probability of the class is not high.

Original: Mean Strategy.
Revised: Mean Strategy.

Original: To evaluate whether our uncertainty estimate is useful, we also consider the following objective which does not incorporate uncertainty: 
Revised: To evaluate whether our uncertainty estimate is useful, we also consider the following objective which does not incorporate uncertainty: arg max l j µ c ( p t , ˆ s t ) .

Original: [Equation]
Revised: [Equation]

Original: Finally
Revised: L

Original: [Table]
Revised: [Table]

Revised: P OLICY

Original:  in order to reach a selected goal in the map [Table] we employ the off-the-shelf deep reinforcement learning model DD-PPO Wijmans et al. (2019) without re-training.
Revised: Finally, in order to reach a selected goal in the map, we employ the off-the-shelf deep reinforcement learning model DD-PPO (Wijmans et al., 2019) without re-training.

Original: This model is trained for the task of point-goal navigation and at each time-step receives the egocentric depth observation and the current goal [Table] and outputs the next navigation action for our agent [Table]
Revised: This model is trained for the task of point-goal navigation and at each time-step receives the egocentric depth observation and the current goal, and outputs the next navigation action for our agent.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: [Table]
Revised: [Table]

Original: We perform experiments on the Matterport3D (MP3D) Chang et al. (2017) dataset using the Habitat Savva et al. (2019) simulator.
Revised: We perform experiments on the Matterport3D (MP3D) (Chang et al., 2017) dataset using the Habitat (Savva et al., 2019) simulator.

Original: 1 MP3D contains reconstructions of real indoor scenes with large appearance and layout variation, and Habitat provides continuous control for realistic agent movement.
Revised: MP3D contains reconstructions of real indoor scenes with large appearance and layout variation, and Habitat provides continuous control for realistic agent movement.

Original: We use the standard train/val split as the test set is held-out for the online Habitat challenge, which contains 56 scenes for training and 11 for validation.
Revised: We use the standard train/val split as the test set is held-out for the online Habitat challenge, which contains 56 scenes for training and 11 for validation.

Original: We conduct three key experiments.
Revised: We conduct three key experiments.

Original: First, we evaluate the performance of our semantic map predictor both in terms of navigation and map quality under different active training strategies (sec. 4.1).
Revised: First, we evaluate the performance of our semantic map predictor both in terms of navigation and map quality under different active training strategies (sec. 4.1).

Original: Second, we compare our method to other navigation strategies on reaching semantic targets and provide ablations over different goal-selection strategies (sec. 4.2).
Revised: Second, we compare our method to other navigation strategies on reaching semantic targets and provide ablations over different goal-selection strategies (sec. 4.2).

Original: Finally, we conduct an error analysis over the effect of the stop decision and local policy in the overall performance (sec. 4.3).
Revised: Finally, we conduct an error analysis over the effect of the stop decision and local policy in the overall performance (sec. 4.3).

Original: For all experiments we use an ensemble size N = 4 .
Revised: For all experiments we use an ensemble size N = 4 .

Revised: Trained models and code can be found here: https://github.com/ggeorgak11/L2M .

Original: We follow the deﬁnition of the object-goal navigation task as described in Batra et al.
Revised: We follow the deﬁnition of the object-goal navigation task as described in Batra et al.

Original: Given a semantic target (e.g. chair) the objective is to navigate to any instance of the target in the scene.
Revised: Given a semantic target (e.g. chair) the objective is to navigate to any instance of the target in the scene.

Original: The agent is spawned at a random location in a novel scene which was not observed during the semantic map predictor training.
Revised: The agent is spawned at a random location in a novel scene which was not observed during the semantic map predictor training.

Original: The agent has access to RGB-D observations, and pose provided by the simulator without noise.
Revised: The agent has access to RGB-D observations and pose provided by the simulator without noise.

Original: We note that estimating the pose from noisy sensor readings is out of the scope of this work and can be addressed by incorporating off-the-shelf visual odometry Zhao et al.
Revised: We note that estimating the pose from noisy sensor readings is out of the scope of this work and can be addressed by incorporating off-the-shelf visual odometry (Zhao et al., 2021).

Original: The action space consists of MOVE_FORWARD by 25 cm , TURN_LEFT and TURN_RIGHT by 10 ◦ and STOP .
Revised: The action space consists of MOVE_FORWARD by 25 cm , TURN_LEFT and TURN_RIGHT by 10 ◦ and STOP .

Original: An episode is successful if the agent selects the STOP action within a certain distance ( 1 m ) from the target and must be completed within a speciﬁc time-budget ( 500 steps).
Revised: An episode is successful if the agent selects the STOP action within a certain distance ( 1 m ) from the target and must be completed within a speciﬁc time-budget ( 500 steps).

Original: Unless otherwise stated, for our experiments we use a representative set of 11 object goal categories present in MP3D: chair , sofa , bed , cushion , counter , table , plant , toilet , tv , cabinet , ﬁreplace and generated 2480 test episodes across the validation scenes.
Revised: Unless otherwise stated, for our experiments we use a representative set of 11 object goal categories present in MP3D: chair , sofa , bed , cushion , counter , table , plant , toilet , tv , cabinet , ﬁreplace and generated 2480 test episodes across the validation scenes.

Original: To evaluate all methods we report the following metrics: (1) Success: percentage of successful episodes, (2) SPL: Success weighted by path length, (3) Soft SPL: Unlike SPL which is 0 for failed episodes, this metric is distance covered towards the goal weighted by path length.
Revised: To evaluate all methods we report the following metrics: (1) Success: percentage of successful episodes, (2) SPL: Success weighted by path length, (3) Soft SPL: Unlike SPL which is 0 for failed episodes, this metric is distance covered [Table]

Revised: Comparisons above the double horizontal line were carried out on our set of 11 objects while the comparison below the double line against SemExp was carried out on the 6 objects reported in [3].

Revised: Note that the discrepancy between the results of SemExp reported here and those in [3] are due to different sets of test episodes.

Revised: towards the goal weighted by path length.

Original: (4) DTS: geodesic distance of agent to the goal at the end of the episode.
Revised: (4) DTS: geodesic distance of agent to the goal at the end of the episode.

Original: Different variants of our method are evaluated in the following experiments.
Revised: Different variants of our method are evaluated in the following experiments.

Original: We follow the naming convention of L2M-X-Y where X , Y correspond to the map predictor training [Table] The comparison to SemExp was carried out on the 6 objects reported in [3].
Revised: We follow the naming convention of L2M-X-Y where X , Y correspond to the map predictor training strategy and the goal-selection strategy respectively.

Original: Note that the discrepancy between the results of SemExp reported here and those in [3] are due to different sets of test episodes.

Original: strategy and the goal-selection strategy respectively.

Original: For example, L2M-Active-UpperBound refers to our proposed method that uses the semantic map predictor after active training (Section 3.2.1) and
Revised: For example, L2M-Active-UpperBound refers to our proposed method that uses the semantic map predictor after active training (Section 3.2.1) and

Original: Eq.
Revised: Eq.

Original: 2 for goal selection.
Revised: 2 for goal selection.

Original: More details of speciﬁc variants are provided in the following sections.
Revised: More details of speciﬁc variants are provided in the following sections.

Original: 4. E VALUATION OVER A CTIVE T RAINING M ETHODS
Revised: 4. E VALUATION OVER A CTIVE T RAINING M ETHODS

Original: We evaluate the impact of our approach to actively training our semantic mapping predictor by comparing different ensemblebased active training strategies over the quality of the predicted maps and object-goal navigation.
Revised: We evaluate the impact of our approach to actively training our semantic mapping predictor by comparing different ensemblebased active training strategies over the quality of the predicted maps and object-goal navigation.

Original: The semantic map prediction is evaluated on the popular segmentation metrics of Intersection over Union (IoU), and F1 score.
Revised: The semantic map prediction is evaluated on the popular segmentation metrics of Intersection over Union (IoU), and F1 score.

Original: We use nine classes: unknown , ﬂoor , wall , bed , chair , cushion , sofa , counter , table .
Revised: We use nine classes: unknown , ﬂoor , wall , bed , chair , cushion , sofa , counter , table .test

Original: 17900 test examples were collected in the 11 validation scenes which had not been observed during training.
Revised: examples were collected in the 11 validation scenes which had not been observed during training.

Original: The evaluation is conducted on predicted map crops of size 64 × 64 that are collected in sequences of length 10.
Revised: The evaluation is conducted on predicted map crops of size 64 × 64 that are collected in sequences of length 10.

Original: While the agent moves within the scene it accumulates multiple views in an occupancy map that is used as input to our method.

Original: The purpose of this experiment is to ascertain the quality of the predicted mapped area around the agent while it is performing a navigation task.
Revised: The purpose of this experiment is to ascertain the quality of the predicted mapped area around the agent while it is performing a navigation task.

Original: Results are shown in Table 1 (object-goal navigation) and Table 2 (map prediction).
Revised: Results are shown in Table 1 (object-goal navigation) and Table 2 (map prediction).

Original: The variant Ofﬂine is our semantic map prediction model without ﬁne-tuning with an active strategy, BALD adapts the BALD objective Houlsby et al. (2011) for actively training our ensemble, and Entropy refers to our model ﬁne-tuned with an entropy objective for active training Shannon (1948).
Revised: The variant Ofﬂine is our semantic map prediction model without ﬁne-tuning with an active strategy, BALD adapts the BALD objective (Houlsby et al., 2011) for actively training our ensemble, and Entropy refers to our model ﬁne-tuned with an entropy objective for active training (Shannon, 1948).

Original: For the navigation comparison all methods use the upper bound objective from Eq.
Revised: For the navigation comparison all methods use the upper bound objective from Eq.

Original: While the baselines report similar performance to each other, our method gains 4 .
Revised: While the baselines report similar performance to each other, our method gains 4 .

Original: 4% in IoU, 6 . 5% in F1, 3 . 9% in Success, and 3 .
Revised: 4% in IoU, 6 . 5% in F1, 3 . 9% in Success, and 3 .

Original: 7% in SPL from the second best method.
Revised: 7% in SPL from the second best method.

Original: This indicates that L2M-Active was more effective in targeting data with high epistemic uncertainty during training and validates our choice for the information gain objective presented in Section 4.1 of the main paper.
Revised: This indicates that L2M-Active is more effective in targeting data with high epistemic uncertainty during training and validates our choice for the information gain objective presented in Section 4.1 of the main paper.

Original: Qualitative results from L2M-Active are presented in Figure 3.
Revised: Figure 3 shows qualitative results of L2M-Active .

Original: 4. C OMPARISONS TO OTHER NAVIGATION METHODS
Revised: 4. C OMPARISONS TO OTHER NAVIGATION METHODS

Original: Here we evaluate L2M against three competitive baselines:
Revised: Here we evaluate L2M against three competitive baselines:

Original: L2M-Ofﬂine-FBE: We combine the classical Frontier-based exploration (FBE) from Yamauchi (1997) for goal selection with our map predictor to facilitate the stop decision.
Revised: L2M-Ofﬂine-FBE: We combine the classical Frontier-based exploration (FBE) from Yamauchi (1997) for goal selection with our map predictor to facilitate the stop decision.

Original:  Segm+ANS+OracleStop: This baseline is adapted from the exploration policy of ANS Chaplot et al. (2020b), anduses our image semantic segmentationmodel to recognize objects.
Revised: [Table] Segm+ANS+OracleStop: This baseline uses Active Neural SLAM (ANS) as an exploration policy to traverse the map and our image semantic segmentation to detect objects.

Original: Since this baseline
Revised: If the target object is detected, the agent navigates to that goal, and an oracle decides to stop the episode if the agent reaches the correct target.

Original: [Table]
Revised: The agent does not have access to a semantic map.

Original: SemExp: The method proposed in Chaplot et al.
Revised: SemExp: The method proposed in Chaplot et al.

Original: (2020a) which was the winner of the CVPR
Revised: (2020a) which was the winner of the CVPR

Original: Habitat ObjectNav Challenge.
Revised: Habitat ObjectNav Challenge.

Original: Since the model that was used in the Habitat challenge is not publicly available, we compare against the six object categories reported in Chaplot et al.
Revised: Since the model that was used in the Habitat challenge is not publicly available, we compare against the six object categories reported in Chaplot et al.

Original: (2020a) using the variant of this method that used Mask R-CNN.
Revised: (2020a) using the variant of this method that used Mask R-CNN.

Original: Furthermore, since the MP3D evaluation episodes used in Chaplot et al. (2020a) are also not available, we ran SemExp on our set of evaluation episodes.
Revised: Furthermore, since the MP3D evaluation episodes used in Chaplot et al. (2020a) are also not available, we ran SemExp on our set of evaluation episodes.

Original: To facilitate a fair comparison, we use the same map resolution between our method and SemExp.

Original: In addition, we evaluate variations of our method with regards to the goal selection. LowerBound , Mixed , and Mean use Eq. 3, 4, and 5 respectively with α 1 = 0 .
Revised: In addition, we evaluate variations of our method ( LowerBound , Mixed , and Mean ) as deﬁned in section 3.3.2 with α 1 = 0 .

Original: 1 and α 2 = 0 .
Revised: 1 and α 2 = 0 .

Original: Our results are presented in Table 3 showing mean and standard error performance over episodes for each of our performance metrics.
Revised: Our results are presented in Table 3.

Original: We observe that our L2M-Active-UpperBound method outperformed all baselines in terms of success rate by a signiﬁcant margin and is comparable to SemExp in terms of SPL.
Revised: We observe that our L2M-Active-UpperBound method outperformed all baselines in terms of success rate by a signiﬁcant margin and is comparable to SemExp in terms of SPL.

Original: This result is not surprising since our upper bound strategy often chooses goals that maximize information gain over the map for effective exploration rather than choosing the shortest path to the goal.
Revised: This result is not surprising since our upper bound strategy often chooses goals that maximize information gain over the map for effective exploration rather than choosing the shortest path to the goal.

Original: Interestingly, L2M-Ofﬂine-FBE outperforms Segm.+ANS+OracleStop even though the latter has access to the stop oracle (which results in a high SPL performance).
Revised: Interestingly, L2M-OfﬂineFBE outperforms Segm.+ANS+OracleStop even though the latter has access to the stop oracle (which results in a high SPL performance).

Original: This demonstrates the advantage of having access to our map prediction module for the object-goal navigation task.
Revised: This demonstrates the advantage of having access to our map prediction module for the object-goal navigation task.

Original: Furthermore, any performance gains of our method towards L2M-Ofﬂine-FBE are a direct result of our goal selection strategies.
Revised: Furthermore, any performance gains of our method towards L2M-Ofﬂine-FBE are a direct result of our goal selection strategies.

Original: Regarding our L2M variations, the upper bound strategy performed best across all metrics.
Revised: Regarding our L2M variations, the upper bound strategy performed best across all metrics.

Original: We note that we expect the mixed and upper bound strategies to have close performance results since by deﬁnition the mixed strategy executes the upper bound strategy whenever the probability of the target class at a given location is less than α 2 = 0 .
Revised: We note that we expect the mixed and upper bound strategies to have close performance results since by deﬁnition the mixed strategy executes the upper bound strategy whenever the probability of the target class at a given location is less than α 2 = 0 .

Original: 4. E RROR A NALYSIS
Revised: 4. E RROR A NALYSIS

Original: A common source of failure in navigation tasks is deciding to stop outside the success radius of the target.
Revised: A common source of failure in navigation tasks is deciding to stop outside the success radius of the target.

Original: In this last experiment we investigate the effect of the stop decision in failure cases of our model by deﬁning an oracle that provides our model with the stop decision within success distance of the target ( OracleStop ).
Revised: In this last experiment we investigate the effect of the stop decision in failure cases of our model by deﬁning an oracle that provides our model with the stop decision within success distance of the target ( OracleStop ).

Original: In addition, we explore the contribution of the local policy in failure cases by replacing it with shortest paths estimated by the habitat simulator towards our selected goals ( GtPath ).
Revised: In addition, we explore the contribution of the local policy in failure cases by replacing it with shortest paths estimated by the habitat simulator towards our selected goals ( GtPath ).

Original: The rest of the components follow our proposed L2M-Active-UpperBound .
Revised: The rest of the components follow our proposed L2M-Active-UpperBound .

Original: The evaluation for this experiment was carried out on a subset of 795 test episodes which are harder due to larger geodesic to euclidean distance ratio between the start position and the target and have larger mean geodesic distance than the rest of our test episodes.
Revised: The evaluation for this experiment was carried out on a subset of 795 test episodes which are harder due to larger geodesic to euclidean distance ratio between the start position and the target and have larger mean geodesic distance than the rest of our test episodes.

Original: Table 4 illustrates our ﬁndings.
Revised: Table 4 illustrates our ﬁndings.

Original: We observe a signiﬁcant increase of performance for all baselines.
Revised: We observe a signiﬁcant increase of performance for all baselines.

Original: In the case of L2M + GtPath the performance gap suggests that the local policy has difﬁculties reaching the goals, while in the case of L2M + OracleSTOP it suggests that our model selects well positioned goals but our stop decision criteria fail to recognize a goal state.
Revised: In the case of L2M + GtPath the performance gap suggests that the local policy has difﬁculties reaching the goals, while in the case of L2M + OracleSTOP it suggests that our model selects well positioned goals but our stop decision criteria fail to recognize a goal state.

Original: Finally, L2M + GtPath + OracleSTOP achieves mean success rate of 80% , thus advising further investigation of these components of our pipeline.
Revised: Finally, L2M + GtPath + OracleSTOP achieves mean success rate of 80% , thus advising further investigation of these components of our pipeline.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: We presented Learning to Map (L2M), a novel framework for object-goal navigation that leverages semantic predictions in unobserved areas of the map to deﬁne uncertainty-based objectives for goal selection.
Revised: We presented Learning to Map (L2M), a novel framework for object-goal navigation that leverages semantic predictions in unobserved areas of the map to deﬁne uncertainty-based objectives for goal selection.

Original: In addition, the uncertainty of the model is used as an information gain objective to actively sample data and train the semantic predictor.
Revised: In addition, the uncertainty of the model is used as an information gain objective to actively sample data and train the semantic predictor.

Original: We investigated different information gain objectives and found epistemic uncertainty to be the most effective for this problem.
Revised: We investigated different information gain objectives and found epistemic uncertainty to be the most effective for this problem.

Original: Furthermore, we proposed multiple goal-selection strategies and observed that balancing exploration and exploitation using upper conﬁdence bounds of our predictions produced higher performance.
Revised: Furthermore, we proposed multiple goal-selection strategies and observed that balancing exploration and exploitation using upper conﬁdence bounds of our predictions produced higher performance.

Original: Finally, our method outperformed competitive baselines on the Matterport3D dataset for object-goal navigation.
Revised: Finally, our method outperformed competitive baselines on the Matterport3D dataset for object-goal navigation.

Original: Ethics Statement.
Revised: Ethics Statement.

Original: Our work advances the capability of autonomous robots to navigate in novel environments which can help create a positive social impact through technologies such as robot caregivers for medically underserved populations.
Revised: Our work advances the capability of autonomous robots to navigate in novel environments which can help create a positive social impact through technologies such as robot caregivers for medically underserved populations.

Original: However, our approach has technical limitations which can yield negative social consequences.
Revised: However, our approach has technical limitations which can yield negative social consequences.

Original: Our semantic hallucination method does not model out-of-distribution scenes and is trained on data from homes in North America and Europe.
Revised: Our semantic hallucination method does not model out-of-distribution scenes and is trained on data from homes in North America and Europe.

Original: If utilized for safety critical tasks such as medical caregiving in hospitals instead of homes or in homes in different regions of the world, our method would act on biases driven by home structure in the training set with potentially harmful outcomes.
Revised: If utilized for safety critical tasks such as medical caregiving in hospitals instead of homes or in homes in different regions of the world, our method would act on biases driven by home structure in the training set with potentially harmful outcomes.

Original: Another technical limitation of our work is our inability to model 3D relationships.
Revised: Another technical limitation of our work is our inability to model 3D relationships.

Original: We ground project 3D information from depth to a 2D map representation for occupancy, thus losing 3D spatial context.
Revised: We ground project 3D information from depth to a 2D map representation for occupancy, thus losing 3D spatial context.

Original: This can be important for objects such as cushions which are often on top of other objects such as sofas.
Revised: This can be important for objects such as cushions which are often on top of other objects such as sofas.

Original: Losing this context potentially contributes to our lower success rate on speciﬁc objects.
Revised: Losing this context potentially contributes to our lower success rate on speciﬁc objects.

Original: Reproducibility Statement.
Revised: Reproducibility Statement.

Original: In Section 4, we include a footnote after the ﬁrst sentence of the section with a link to our GitHub repository.
Revised: In Section 4, we include a footnote after the ﬁrst sentence of the section with a link to our GitHub repository.

Original: This repository includes the code for our models and instructions for reproducing our results.
Revised: This repository includes the code for our models and instructions for reproducing our results.

Original: We provide both a Docker image with the dependencies for running our code and instructions to install the required dependencies without using Docker.
Revised: We provide both a Docker image with the dependencies for running our code and instructions to install the required dependencies without using Docker.

Original: We give instructions for generating initial training data from the Habitat simulator as well as instructions for collecting training data using our active policy.
Revised: We give instructions for generating initial training data from the Habitat simulator as well as instructions for collecting training data using our active policy.

Original: We have instructions for training and testing all of the model variants described in our work.
Revised: We have instructions for training and testing all of the model variants described in our work.

Original: We provide Google Drive links to the test episodes we used to evaluate our models as well as the MP3D scene point clouds which include the semantic category labels for the 3D point clouds.
Revised: We provide Google Drive links to the test episodes we used to evaluate our models as well as the MP3D scene point clouds which include the semantic category labels for the 3D point clouds.

Original: Each of our trained models is also shared via Google Drive links, and we link to the pre-trained DD-PPO model provided by the authors of that work which we leverage in our experiments.
Revised: Each of our trained models is also shared via Google Drive links, and we link to the pre-trained DD-PPO model provided by the authors of that work which we leverage in our experiments.

Original: In addition, implementation details can be found in section A.1 of the Appendix where we describe the hyperparameter values used during our experiments, the training procedure that we followed for the semantic map predictors, and we provide the pseudo-code algorithm for the execution of our method during a navigation episode. 
Revised: In addition, implementation details can be found in section A.1 of the Appendix where we describe the hyperparameter values used during our experiments, the training procedure that we followed for the semantic map predictors, and we provide the pseudo-code algorithm for the execution of our method during a navigation episode. A CKNOWLEDGMENTS

Revised: We would like to thank Samuel Xu for implementation support in evaluating baseline methods.

Revised: Research was sponsored by the Honda Research Institute through the Curious Minded Machines project, by the Army Research Ofﬁce under Grant Number W911NF-20-1-0080 and by the National

Revised: Science Foundation under grants CPS 2038873, NSF IIS 1703319.

Original: A A PPENDIX
Revised: A A PPENDIX

Original: Here we provide the following additional material:
Revised: Here we provide the following additional material:

Original: Implementation details.
Revised: Implementation details.

Original: Additional experimental results for semantic map prediction.
Revised: Additional experimental results for semantic map prediction.

Original: Per-object navigation results for the different active training strategies.
Revised: Per-object navigation results for the different active training strategies.

Original: Per-object error analysis over stop decision and local policy.
Revised: Per-object error analysis over stop decision and local policy.

Original: Evaluation of our method on easy vs hard episodes.
Revised: Evaluation of our method on easy vs hard episodes.

Original: Additional visualizations of semantic maps and navigation examples.
Revised: Additional visualizations of semantic maps and navigation examples.

Original: A. I MPLEMENTATION D ETAILS
Revised: A. I MPLEMENTATION D ETAILS

Original: [Table]

Original: Algorithm 1: L2M for ObjectNav
Revised: Algorithm 1: L2M for ObjectNav

Original: Input: Semantic target c ;
Revised: Input: Semantic target c ;

Original: Time t = 0 with current position x t ;
Revised: Time t = 0 with current position x t ;

Original: Stop decision probability threshold S p ;
Revised: Stop decision probability threshold S p ;

Original: Stop decision distance threshold S d ;
Revised: Stop decision distance threshold S d ;

Original: Replan interval R ; while t < max_steps_per_episode do
Revised: Replan interval R ; while t < max_steps_per_episode do

Original: Observe RGB I t , occupancy p t ;
Revised: Observe RGB I t , occupancy p t ;

Original: Segment I t and ground project to compute ˆ s t ;
Revised: Segment I t and ground project to compute ˆ s t ;

Original: Hallucinate semantic map ˆ m t,c = µ c ( p t , ˆ s t ) ;
Revised: Hallucinate semantic map ˆ m t,c = µ c ( p t , ˆ s t ) ;

Original: Estimate uncertainty; if (Goal is reached) or (t mod R == 0) then
Revised: Estimate uncertainty; if (Goal is reached) or (t mod R == 0) then

Original: Compute Eq.
Revised: Compute Eq.

Original: 2 to select goal l ∗ ; endif ˆ m t,c > S p at l j on path to l ∗ then if D ( x t , l j ) < S d then
Revised: 2 to select goal l ∗ ; endif ˆ m t,c > S p at l j on path to l ∗ then if D ( x t , l j ) < S d then

Original: Stop decision; end end
Revised: Stop decision; end end

Original: Navigate toward goal with DD-PPO; t = t + 1 ; end
Revised: Navigate toward goal with DD-PPO; t = t + 1 ; end

Original: All UNets Ronneberger et al.
Revised: All UNets Ronneberger et al.

Original: (2015) in our implementation are combined with a backbone ResNet18 He et al.
Revised: (2015) in our implementation are combined with a backbone ResNet18 He et al.

Original: for providing initial encodings of the inputs.
Revised: for providing initial encodings of the inputs.

Original: Each UNet has four encoder and four decoder convolutional blocks with skip connections.
Revised: Each UNet has four encoder and four decoder convolutional blocks with skip connections.

Original: The models are trained in the PyTorch Paszke et al.
Revised: The models are trained in the PyTorch Paszke et al.

Original: (2017) framework with Adam optimizer and a learning rate of 0.0002.
Revised: (2017) framework with Adam optimizer and a learning rate of 0.0002.

Original: All experiments are conducted with an ensemble size N = 4 .
Revised: All experiments are conducted with an ensemble size N = 4 .

Original: For the semantic map prediction we receive RGB and depth observations of size 256 × 256 and deﬁne crop and global map dimensions as h = w = 64 , H = W = 384 respectively.
Revised: For the semantic map prediction we receive RGB and depth observations of size 256 × 256 and deﬁne crop and global map dimensions as h = w = 64 , H = W = 384 respectively.

Original: We use
Revised: We use

Original: C s = 27 semantic classes that we selected from the original 40 categories of MP3D Chang et al.
Revised: C s = 27 semantic classes that we selected from the original 40 categories of MP3D Chang et al.

Original: We generate the ground-truth for semantic crops using the 3D point clouds of the scenes which contain semantic labels.
Revised: We generate the ground-truth for semantic crops using the 3D point clouds of the scenes which contain semantic labels.

Original: We executed training and testing on our internal cluster on RTX 2080 Ti GPUs.
Revised: We executed training and testing on our internal cluster on RTX 2080 Ti GPUs.

Original: To train our ﬁnal model, our image segmentation network ﬁrst trained for 24 hours.
Revised: To train our ﬁnal model, our image segmentation network ﬁrst trained for 24 hours.

Original: Then, each model in our ofﬂine ensemble trained for 72 hours on separate GPUs.
Revised: Then, each model in our ofﬂine ensemble trained for 72 hours on separate GPUs.

Original: Finally, each model was ﬁne-tuned on the actively collected data for 24 hours on separate GPUs.
Revised: Finally, each model was ﬁne-tuned on the actively collected data for 24 hours on separate GPUs.

Original: Regarding navigation, we use a threshold of 0 .
Revised: Regarding navigation, we use a threshold of 0 .

Original: 75 on the prediction probabilities to determine the occurrence of the target object in the map, and a stop decision distance of 0 .
Revised: 75 on the prediction probabilities to determine the occurrence of the target object in the map, and a stop decision distance of 0 .

Original: 5 m .
Revised: 5 m .

Original: Finally, we re-select a goal every 20 steps.
Revised: Finally, we re-select a goal every 20 steps.

Original: Algorithm 1 shows additional details regarding the execution of our method during an object-goal navigation episode.
Revised: Algorithm 1 shows additional details regarding the execution of our method during an object-goal navigation episode.

Original: A. S EMANTIC M AP P REDICTION
Revised: A. S EMANTIC M AP P REDICTION

Original: Here we provide additional results for our semantic map predictor, including evaluation over occupancy predictions ( unknown , occupied , free ).
Revised: Here we provide additional results for our semantic map predictor, including evaluation over occupancy predictions ( unknown , occupied , free ).

Original: The set-up for this experiment is the same as in
Revised: The set-up for this experiment is the same as in

Original: Section 4.1 of the main paper.
Revised: Section 4.1 of the main paper.

Original: The purpose of this evaluation is to demonstrate the superiority of our method to possible non-prediction alternatives such as using directly the projected depth, or ground-projected image segmentation.
Revised: The purpose of this evaluation is to demonstrate the superiority of our method to possible non-prediction alternatives such as using directly the projected depth, or ground-projected image segmentation.

Original: To this end we compare against the following baselines:
Revised: To this end we compare against the following baselines:

Original: • Depth Projection: Occupancy map estimated from a single depth observation.
Revised: • Depth Projection: Occupancy map estimated from a single depth observation.

Original: • Multi-view Depth Projection: Occupancy map accumulated over multiple views of depth observations.
Revised: • Multi-view Depth Projection: Occupancy map accumulated over multiple views of depth observations.

Original: • Image Segmentation Projection: Our semantic segmentation model that operates on image observations, followed by projection of resulting labels.
Revised: • Image Segmentation Projection: Our semantic segmentation model that operates on image observations, followed by projection of resulting labels.

Revised: [Table]

Original: • Semantic Sensor Projection: Semantic map generated by single-view ground-truth semantic sensor images provided by the simulator.
Revised: • Semantic Sensor Projection: Semantic map generated by single-view ground-truth semantic sensor images provided by the simulator.

Original: • Multi-view Semantic Sensor Projection:
Revised: • Multi-view Semantic Sensor Projection:

Original: Same as the previous baseline, but with multiple views accumulated in the semantic map.
Revised: Same as the previous baseline, but with multiple views accumulated in the semantic map.

Original: We present mean values for accuracy, intersection over union (IoU), and F1 score in Table 5.
Revised: We present mean values for accuracy, intersection over union (IoU) [Table] and F1 score in Table 5.

Original: Both our approaches outperform all baselines by a signiﬁcant margin.
Revised: Both our approaches outperform all baselines by a signiﬁcant margin.

Original: This suggests that our predictions of unobserved areas can provide more useful information to the agent than only relying on accumulated views from egomotion.
Revised: This suggests that our predictions of unobserved areas can provide more useful information to the agent than only relying on accumulated views from egomotion.

Original: In the case of semantic prediction our results are even more compelling as they are compared against the ground-truth semantic sensor of the Habitat simulator.
Revised: In the case of semantic prediction our results are even more compelling as they are compared against the ground-truth semantic sensor of the Habitat simulator.

Original: Note that the
Revised: Note that the

Original: Multi-view Sensor baseline gets far from perfect score for two reasons: 1) it still contains unobserved areas, and 2) due to the pooling of the labels in the lower spatial dimensions of the top-down map (which affects all projections).
Revised: Multi-view Sensor baseline gets far from perfect score for two reasons: 1) it still contains unobserved areas [Table] and 2) due to the pooling of the labels in the lower spatial dimensions of the top-down map (which affects all projections).

Original: In contrast, our approach is unaffected by this problem as we learn to predict semantics from depth projected inputs.
Revised: In contrast, our approach is unaffected by this problem as we learn to predict semantics from depth projected inputs.

Original: A. P ER O BJECT E VALUATION FOR A CTIVE T RAINING M ETHODS
Revised: A. P ER O BJECT E VALUATION FOR A CTIVE T RAINING M ETHODS

Original: In this section we show per object results over map prediction quality and object-goal navigation for the different active training strategies ( L2M-BALD , L2M-Entropy , L2M-Ofﬂine ) introduced in Section 4.1 of the main paper against our proposed L2M-Active .
Revised: In this section we show per object results over map prediction quality and object-goal navigation for the different active training strategies ( L2M-BALD , L2M-Entropy , L2M-Ofﬂine ) introduced in Section 4.1 of the main paper against our proposed L2M-Active .

Original: We show results over a representative subset of six objects chair , bed , cushion , counter , sofa , table in Figure 4 (map prediction) and Figure(object-goal navigation).
Revised: We show results over a representative subset of six objects chair , bed , cushion , counter , sofa , table in Figure 4 (map prediction) and Figure(object-goal navigation).

Original: In both cases we observe that the performance gap between L2M-Active and the baselines is larger on more challenging target classes such as cushion and counter.
Revised: In both cases we observe that the performance gap between L2M-Active and the baselines is larger on more challenging target classes such as cushion and counter.

Original: This suggests that our active training successfully selected examples with high information value as opposed to
Revised: This suggests that our active training successfully selected examples with high information value as opposed to

Original: L2M-BALD and L2M-Entropy which do not show signiﬁcant improvement over L2M-Ofﬂine .
Revised: L2M-BALD and L2M-Entropy which do not show signiﬁcant improvement over L2M-Ofﬂine .

Original: A. P ER O BJECT E RROR A NALYSIS
Revised: [Figure]

Original: Here we present additional results to the experiment in section 4.3 where we investigated the effect of the stop decision and local policy in failure cases of our model.
Revised: Here we present additional results to the experiment in section 4.3 where we investigated the effect of the stop decision and local policy in failure cases of our model.

Original: To do so, we combined our L2MActive method with an oracle that stops the agent within success distance of the target ( OracleStop ) and replaced the local policy with shortest paths estimated by the habitat simulator to reach our selected goals ( GtPath ).
Revised: To do so, we combined our L2MActive method with an oracle that stops the agent within success distance of the target ( OracleStop ) and replaced the local policy with shortest paths estimated by the habitat simulator to reach our selected goals ( GtPath ).

Original: Results are shown in Figure 6.
Revised: Results are shown in Figure 6.

Original: The largest performance improvement (especially for SPL) is seen when the OracleStop is enabled, as opposed to GtPath , suggesting that a very common failure case is recognizing that we’ve reached the target.
Revised: The largest performance improvement (especially for SPL) is seen when the OracleStop is enabled, as opposed to GtPath , suggesting that a very common failure case is recognizing that we’ve reached the target.

Original: Perhaps unsurprisingly, this is also more pronounced in categories where our map predictor seems to be underperforming (see
Revised: Perhaps unsurprisingly, this is also more pronounced in categories where our map predictor seems to be underperforming (see

Original: Figure 4) such as cushion, counter and table.
Revised: Figure 4) such as cushion, counter and table.

Original: This result also indicates that our method selects well positioned goals across all object categories, but we often fail to either reach the target due to errors in the local policy or fail to recognize a goal state.
Revised: This result also indicates that our method selects well positioned goals across all object categories, but we often fail to either reach the target due to errors in the local policy or fail to recognize a goal state.

Original: A. E ASY VS H ARD E PISODES
Revised: A. E ASY VS H ARD E PISODES

Original: Another way of evaluating the impact of our proposed method is by analyzing its performance with respect to easy and hard episodes.
Revised: Another way of evaluating the impact of our proposed method is by analyzing its performance with respect to easy and hard episodes.

Original: We generated 1685 easy and 795 hard episodes, which combined make up our entire test set used in Section 4 of the main paper.
Revised: We generated 1685 easy and 795 hard episodes, which combined make up our entire test set used in Section 4 of the main paper.

Original: The hard episodes are generated with larger geodesic to euclidean distance ratio between the starting position and the target ( 1 . 1 vs 1 . 05 ), which translates to more obstacles present in the path, and have larger mean geodesic distance ( 6 .
Revised: The hard episodes are generated with larger geodesic to euclidean distance ratio between the starting position and the target ( 1 . 1 vs 1 . 05 ), which translates to more obstacles present in the path, and have larger mean geodesic distance ( 6 .

Original: 5 m vs 4 .
Revised: 5 m vs 4 .

Original: 5 m ).
Revised: 5 m ).

Original: The results are visualized in Figure 7.
Revised: The results are visualized in Figure 7.

Original: It is worth noting that the performance gap is higher in the case of hard .
Revised: It is worth noting that the performance gap is higher in the case of hard .

Original: Our actively trained semantic mapping model seeks out difﬁcult data during training, resulting in more consistently high performance on both easy and hard episodes than the models trained only with ofﬂine data ( L2M-Ofﬂine ), or using different information-gain objectives -BALD
Revised: Our actively trained semantic mapping model seeks out difﬁcult data during training, resulting in more consistently high performance on both easy and hard episodes than the models trained only with ofﬂine data ( L2M-Ofﬂine ), or using different information-gain objectives ( L2M

Original: A. A DDITIONAL VISUALIZATIONS
Revised: A. A DDITIONAL VISUALIZATIONS

Original: Finally, we provide some additional visualizations.
Revised: Finally, we provide some additional visualizations.

Original: Example navigation episodes are shown in
Revised: Example navigation episodes are shown in

Original: Figure 8.
Revised: Figure 8.

Original: A set of semantic predictions are shown in Figure 9, while Figure 10 showcases predictions from the individual models in the ensemble, qualitatively demonstrating the variation within the ensemble over semantic predictions.
Revised: A set of semantic predictions are shown in Figure 9, while Figure 10 showcases predictions from the individual models in the ensemble, qualitatively demonstrating the variation within the ensemble over semantic predictions.