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Causality-Aware Transformer Networks for Robotic Navigation

Ruoyu Wang, Yao Liu, Yuanjiang Cao, Lina Yao

TL;DR

This work proposes Causality-Aware Transformer (CAT) Networks for Navigation, featuring a Causal Understanding Module to enhance the models's Environmental Understanding capability, and is devoid of task-specific inductive biases and can be trained in an End-to-End manner, which enhances the method's generalizability across various contexts.

Abstract

Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Embodied AI and traditional sequential data modelling, potentially limiting its performance in Embodied AI tasks. Second, the reliance on task-specific configurations, such as pre-trained modules and dataset-specific logic, compromises the generalizability of these methods. We address these constraints by initially exploring the unique differences between Navigation tasks and other sequential data tasks through the lens of Causality, presenting a causal framework to elucidate the inadequacies of conventional sequential methods for Navigation. By leveraging this causal perspective, we propose Causality-Aware Transformer (CAT) Networks for Navigation, featuring a Causal Understanding Module to enhance the models's Environmental Understanding capability. Meanwhile, our method is devoid of task-specific inductive biases and can be trained in an End-to-End manner, which enhances the method's generalizability across various contexts. Empirical evaluations demonstrate that our methodology consistently surpasses benchmark performances across a spectrum of settings, tasks and simulation environments. Extensive ablation studies reveal that the performance gains can be attributed to the Causal Understanding Module, which demonstrates effectiveness and efficiency in both Reinforcement Learning and Supervised Learning settings.

Causality-Aware Transformer Networks for Robotic Navigation

TL;DR

This work proposes Causality-Aware Transformer (CAT) Networks for Navigation, featuring a Causal Understanding Module to enhance the models's Environmental Understanding capability, and is devoid of task-specific inductive biases and can be trained in an End-to-End manner, which enhances the method's generalizability across various contexts.

Abstract

Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Embodied AI and traditional sequential data modelling, potentially limiting its performance in Embodied AI tasks. Second, the reliance on task-specific configurations, such as pre-trained modules and dataset-specific logic, compromises the generalizability of these methods. We address these constraints by initially exploring the unique differences between Navigation tasks and other sequential data tasks through the lens of Causality, presenting a causal framework to elucidate the inadequacies of conventional sequential methods for Navigation. By leveraging this causal perspective, we propose Causality-Aware Transformer (CAT) Networks for Navigation, featuring a Causal Understanding Module to enhance the models's Environmental Understanding capability. Meanwhile, our method is devoid of task-specific inductive biases and can be trained in an End-to-End manner, which enhances the method's generalizability across various contexts. Empirical evaluations demonstrate that our methodology consistently surpasses benchmark performances across a spectrum of settings, tasks and simulation environments. Extensive ablation studies reveal that the performance gains can be attributed to the Causal Understanding Module, which demonstrates effectiveness and efficiency in both Reinforcement Learning and Supervised Learning settings.
Paper Structure (21 sections, 1 theorem, 5 equations, 4 figures, 4 tables)

This paper contains 21 sections, 1 theorem, 5 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Motivation
  4. Motivation Formulation from a Causal Perspective
  5. Causality-Aware Transformer Networks
  6. Experiments
  7. Experiment Setting
  8. Task Descriptions
  9. Evaluation Metrics
  10. Baselines
  11. Environment and Parameters
  12. Results
  13. Ablation Studies
  14. Impact of Causal Understanding Module
  15. Impact of Multi-modal Transformer
  16. ...and 6 more sections

Key Result

proposition thmcounterproposition

At any given time step $t$, and for any integer $\delta \geq 2$, there exist no direct causal relationships between $S_{t}$ and $S_{t-\delta}$, the causal relationships between states $S_{t}$ and $S_{t-\delta}$ are indirect and must be mediated by states $S_{t'}$ for all $t'$ where $t-\delta \leq t'

Figures (4)

  • Figure 1: Our method encourages the model to understand the environment by highlighting the direct causal relationships and diminishing the non-direct causal associations.
  • Figure 2: The framework of our proposed method. First, we process the visual states by the CLIP vision model and process the objective and previous actions by simple Embedding modules. After a tunable Feature Post-Processing layer, we concatenate the features of the states and the actions and process the features with a Transformer Encoder. Finally, the Actor layer takes the post-processed visual features as input to predict the action at the current time step.
  • Figure 3: Effect of our method. (a)-(b) Comparison on RoboTHOR ObjNav Find an AlarmClock task. Our method encourages the agent to stop at a spot closer to the goal object, thus benefiting the performance (c)-(d) Comparison on Habitat PointNav task. Our method allows the agent to directly navigate to the target point by choosing an optimal route.
  • Figure 4: Average Success Rate for EmbCLIP and Causal-RNN on RoboTHOR ObjNav. Our proposed Causal Understanding Module can: 1) significantly benefit the performance; and 2) significantly reduce the training time by 10 times.

Theorems & Definitions (1)

  • proposition thmcounterproposition