Symmetry-aware Neural Architecture for Embodied Visual Navigation

• URL: http://arxiv.org/abs/2112.09515v1
• Date: Fri, 17 Dec 2021 14:07:23 GMT
• Title: Symmetry-aware Neural Architecture for Embodied Visual Navigation
• Authors: Shuang Liu and Takayuki Okatani
• Abstract summary: Experimental results show that our method increases area coverage by $8.1 m2$ when trained on the Gibson dataset and tested on the MP3D dataset.
• Score: 24.83118298491349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual exploration is a task that seeks to visit all the navigable areas of an environment as quickly as possible. The existing methods employ deep reinforcement learning (RL) as the standard tool for the task. However, they tend to be vulnerable to statistical shifts between the training and test data, resulting in poor generalization over novel environments that are out-of-distribution (OOD) from the training data. In this paper, we attempt to improve the generalization ability by utilizing the inductive biases available for the task. Employing the active neural SLAM (ANS) that learns exploration policies with the advantage actor-critic (A2C) method as the base framework, we first point out that the mappings represented by the actor and the critic should satisfy specific symmetries. We then propose a network design for the actor and the critic to inherently attain these symmetries. Specifically, we use $G$-convolution instead of the standard convolution and insert the semi-global polar pooling (SGPP) layer, which we newly design in this study, in the last section of the critic network. Experimental results show that our method increases area coverage by $8.1 m^2$ when trained on the Gibson dataset and tested on the MP3D dataset, establishing the new state-of-the-art.

Related papers

• BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping [64.8477128397529]
  We propose a training-required and training-free test-time adaptation framework. We maintain a light-weight key-value memory for feature retrieval from instance-agnostic historical samples and instance-aware boosting samples. We theoretically justify the rationality behind our method and empirically verify its effectiveness on both the out-of-distribution and the cross-domain datasets.
  arXiv  Detail & Related papers  (2024-10-20T15:58:43Z)
• No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery [53.08822154199948]
  Unsupervised Environment Design (UED) methods have gained recent attention as their adaptive curricula promise to enable agents to be robust to in- and out-of-distribution tasks. This work investigates how existing UED methods select training environments, focusing on task prioritisation metrics. We develop a method that directly trains on scenarios with high learnability.
  arXiv  Detail & Related papers  (2024-08-27T14:31:54Z)
• Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm [0.195804735329484]
  Reinforcement learning (RL) and Deep Reinforcement Learning (DRL) have the potential to disrupt and are already changing the way we interact with the world. One of the key indicators of their applicability is their ability to scale and work in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-19T14:50:48Z)
• Learning with Noisy Foundation Models [95.50968225050012]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets. We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design [11.922951794283168]
  In this work, we investigate how the sampling of individual environment instances, or levels, affects the zero-shot generalisation (ZSG) ability of RL agents. We discover that for deep actor-critic architectures sharing their base layers, prioritising levels according to their value loss minimises the mutual information between the agent's internal representation and the set of training levels in the generated training data. We find that existing UED methods can significantly shift the training distribution, which translates to low ZSG performance. To prevent both overfitting and distributional shift, we introduce data-regularised environment design (D
  arXiv  Detail & Related papers  (2024-02-05T19:47:45Z)
• Leveraging Data Geometry to Mitigate CSM in Steganalysis [1.130790932059036]
  In operational scenarios, steganographers use sets of covers from various sensors and processing pipelines that differ significantly from those used by researchers to train steganalysis models. This leads to an inevitable performance gap when dealing with out-of-distribution covers, commonly referred to as Cover Source Mismatch (CSM) In this study, we consider the scenario where test images are processed using the same pipeline. Our objective is to identify a training dataset that allows for maximum generalization to our target.
  arXiv  Detail & Related papers  (2023-10-06T09:08:25Z)
• A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes [58.633364000258645]
  We call this dataset RIVAL10 consisting of roughly $26k$ instances over $10$ classes. We evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In our analysis, we consider diverse state-of-the-art architectures (ResNets, Transformers) and training procedures (CLIP, SimCLR, DeiT, Adversarial Training)
  arXiv  Detail & Related papers  (2022-01-26T06:31:28Z)
• Neural Topological SLAM for Visual Navigation [112.73876869904]
  We design topological representations for space that leverage semantics and afford approximate geometric reasoning. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.
  arXiv  Detail & Related papers  (2020-05-25T17:56:29Z)
• Diagnosing the Environment Bias in Vision-and-Language Navigation [102.02103792590076]
  Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. Recent works that study VLN observe a significant performance drop when tested on unseen environments, indicating that the neural agent models are highly biased towards training environments. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias.
  arXiv  Detail & Related papers  (2020-05-06T19:24:33Z)
• Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation [18.519303422753534]
  We argue that this ability is largely due to three main reasons: the incorporation of prior knowledge (or experience), the adaptation of it to the new environment using the observed visual cues and optimistically searching without giving up early. This is currently missing in the state-of-the-art visual navigation methods based on Reinforcement Learning (RL) In this paper, we propose to use externally learned prior knowledge of the relative object locations and integrate it into our model by constructing a neural graph.
  arXiv  Detail & Related papers  (2020-04-07T09:31:07Z)
• Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation [0.0]
  This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences.
  arXiv  Detail & Related papers  (2020-02-25T08:04:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.