The generalization of the end-to-end deep reinforcement learning (DRL) for
object-goal visual navigation is a long-standing challenge since object classes
and placements vary in new test environments. Learning domain-independent
visual representation is critical for enabling the trained DRL agent with the
ability to generalize to unseen scenes and objects. In this letter, a
target-directed attention network (TDANet) is proposed to learn the end-to-end
object-goal visual navigation policy with zero-shot ability. TDANet features a
novel target attention (TA) module that learns both the spatial and semantic
relationships among objects to help TDANet focus on the most relevant observed
objects to the target. With the Siamese architecture (SA) design, TDANet
distinguishes the difference between the current and target states and
generates the domain-independent visual representation. To evaluate the
navigation performance of TDANet, extensive experiments are conducted in the
AI2-THOR embodied AI environment. The simulation results demonstrate a strong
generalization ability of TDANet to unseen scenes and target objects, with
higher navigation success rate (SR) and success weighted by length (SPL) than
other state-of-the-art models.

提出了一种目标导向的注意网络（TDANet），用于学习具有零 - shot 能力的端到端目标导向视觉导航策略，并通过在 AI2-THOR 环境中进行的大量实验验证了 TDANet 的导航性能，其在未见过的场景和目标对象上表现出较高的导航成功率和成功率加权长度，优于其他最先进的模型。

TDANet：面向目标导向的注意力网络在具备零样本能力的目标 - 目标视觉导航中的应用

TDANet: Target-Directed Attention Network For Object-Goal Visual  Navigation With Zero-Shot Ability

End-to-end deep reinforcement learning (DRL) for quadrotor control promises
many benefits -- easy deployment, task generalization and real-time execution
capability. Prior end-to-end DRL-based methods have showcased the ability to
deploy learned controllers onto single quadrotors or quadrotor teams
maneuvering in simple, obstacle-free environments. However, the addition of
obstacles increases the number of possible interactions exponentially, thereby
increasing the difficulty of training RL policies. In this work, we propose an
end-to-end DRL approach to control quadrotor swarms in environments with
obstacles. We provide our agents a curriculum and a replay buffer of the
clipped collision episodes to improve performance in obstacle-rich
environments. We implement an attention mechanism to attend to the neighbor
robots and obstacle interactions - the first successful demonstration of this
mechanism on policies for swarm behavior deployed on severely
compute-constrained hardware. Our work is the first work that demonstrates the
possibility of learning neighbor-avoiding and obstacle-avoiding control
policies trained with end-to-end DRL that transfers zero-shot to real
quadrotors. Our approach scales to 32 robots with 80% obstacle density in
simulation and 8 robots with 20% obstacle density in physical deployment. Video
demonstrations are available on the project website at:
this https URL

在包含障碍物的环境中，我们提出一种使用端到端深度强化学习（DRL）控制无人机群的方法，该方法通过提供课程和剪辑碰撞情景的重放缓冲区来改善性能，并且实现了对邻居机器人和障碍物交互的注意机制。这是首次成功地将该机制应用于在计算资源受限的硬件上部署的群体行为策略，我们的方法可以在模拟环境中实现 32 架机器人并达到 80％的障碍物密度，在实际物理环境中可以实现 8 架机器人并达到 20％的障碍物密度，并且可以零 - shot 地转移到真实的无人机上。