通过带碰撞预测的两阶段训练提高物体目标视觉导航的无碰撞成功率
Improving Collision-Free Success Rate For Object Goal Visual Navigation Via Two-Stage Training With Collision Prediction
AI总结：
针对RGB物体目标视觉导航中碰撞被成功评估忽略且碰撞惩罚易导致策略保守的问题，提出带碰撞预测的两阶段训练方法，显著提升多种模型的无碰撞成功率。
摘要：
物体目标视觉导航是利用第一人称视角视觉观察导航到特定目标物体的任务。近期基于深度强化学习的端到端导航模型在寻找并到达目标物体方面取得了显著性能。然而，这些模型在导航过程中的碰撞问题仍未解决，因为在评估成功时通常会忽略碰撞。尽管在训练期间为碰撞加入负奖励看似直接，但这会产生更保守的策略，从而限制智能体到达目标的能力。此外，这些模型中许多仅使用RGB观察，由于缺少深度信息，进一步增加了避碰难度。为解决这些局限，本文引入了一个新概念——无碰撞成功，用于评估导航模型寻找通往目标物体的无碰撞路径的能力。提出了一种带碰撞预测的两阶段训练方法，以提高现有使用RGB观察的导航模型的无碰撞成功率。在第一个训练阶段，碰撞预测模块监督智能体在探索期间的碰撞状态，以学习预测可能的碰撞。在第二阶段，智能体利用训练好的碰撞预测，学习在不发生碰撞的情况下导航到目标。AI2-THOR环境中的实验结果表明，所提方法大幅提高了不同导航模型的无碰撞成功率，并且优于其他可比较的避碰方法。
英文摘要：
The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent's ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept -- collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent's collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
