Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation (2301.00362v2)

Abstract: Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-the-art (SOTA) baselines. The demonstration video (https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code (https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are also provided.

• The paper introduces a novel Goal-guided Transformer that integrates goal information into scene encoding to improve deep reinforcement learning efficiency.
• The method uses expert demonstrations for pre-training, leading to faster convergence and superior performance in both simulated and real-world navigation tasks.
• Experimental ablation and interpretability analyses, including attention flow maps and unsupervised metrics, validate the robust and focused representation of goal-relevant features.

Overview of the "Goal-guided Transformer-enabled Reinforcement Learning for Efficient Autonomous Navigation" Paper

This paper presents a paper on improving data efficiency in autonomous navigation using deep reinforcement learning (DRL). The authors address the common challenge faced by DRL-based approaches in navigation tasks, where goal information is often decoupled from scene perception. This decoupling leads to inefficient learning as goal-irrelevant features may adversely impact the training process. To resolve this issue, the paper introduces a novel methodology that integrates goal information directly into the scene representation process through a newly proposed system called Goal-guided Transformer-enabled reinforcement learning (GTRL).

The key innovation in this work is the design of the Goal-guided Transformer (GoT), a variant of the Vision Transformer (ViT), which incorporates physical goal states into the scene encoding process. This integration aims to enhance the alignment between scene representation and goal objectives. The GoT is pre-trained using expert demonstrations, serving as 'expert priors', to increase data efficiency before engaging in the reinforcement learning phase. As a result, the DRL approach is designed to focus predominantly on goal-relevant features, purportedly leading to improved autonomous navigation performance.

Significant results reported include enhanced data efficiency and superior performance metrics both in simulations and real-world settings, compared with state-of-the-art (SOTA) DRL models. The proposed GTRL approach, particularly in the configuration using the GoT structure, shows promise in sim-to-real transfer tasks, demonstrating robustness in previously unencountered conditions.

The paper details the methodologically rich evaluation conducted using simulation environments followed by real-world experiments on unmanned ground vehicles (UGVs). The quantitative assessments draw comparisons using baseline models like ConvNet-SAC and ViT-SAC, showcasing how GoT-SAC achieves a faster convergence and better success rates in autonomous navigation tasks. An ablation paper on the GoT architecture underscores the trade-off considerations in transformer design, particularly focusing on the number of self-attention heads and encoder blocks. Furthermore, using visual attention flow maps and unsupervised metrics such as the Gini coefficient and Shannon-Wiener index, the interpretability of the goal-oriented scene representation is rigorously analyzed, warranting a more concentrated and effective attention mechanism than baseline models.

The implications of these findings are twofold. Practically, the results promise advancements in goal-driven navigation strategies, possibly extending to more dynamic environments with little prior mapping. Theoretically, this integration of transformer models with reinforcement learning systems could signal a shift in how scene representations are aligned with objective functions in RL tasks. Future research in this domain could explore the scalability of the GTRL to larger-scale and more complex task environments or delve into integrating more complex multimodal data inputs to further improve navigation decision-making capabilities. The proposed framework may also serve as a foundation for developing more robust models that are pre-trained across diverse tasks before fine-tuning on specific navigation objectives.