The domain of Embodied AI has recently witnessed substantial progress,
particularly in navigating agents within their environments. These early
successes have laid the building blocks for the community to tackle tasks that
require agents to actively interact with objects in their environment. Object
manipulation is an established research domain within the robotics community
and poses several challenges including manipulator motion, grasping and
long-horizon planning, particularly when dealing with oft-overlooked practical
setups involving visually rich and complex scenes, manipulation using mobile
agents (as opposed to tabletop manipulation), and generalization to unseen
environments and objects. We propose a framework for object manipulation built
upon the physics-enabled, visually rich AI2-THOR framework and present a new
challenge to the Embodied AI community known as ArmPointNav. This task extends
the popular point navigation task to object manipulation and offers new
challenges including 3D obstacle avoidance, manipulating objects in the
presence of occlusion, and multi-object manipulation that necessitates long
term planning. Popular learning paradigms that are successful on PointNav
challenges show promise, but leave a large room for improvement.

本文提出了一个基于 AI2-THOR 框架的物体操作框架并提出了一个名为 ArmPointNav 的新挑战任务，以扩展点导航任务到物体操作，并提供了新的挑战，包括 3D 障碍物避免、存在遮挡的物体操作以及多物体操作，并指出了 PointNav 挑战中成功的流行学习范例仍有大量改进空间。

ManipulaTHOR：一个用于视觉物体操纵的框架

ManipulaTHOR: A Framework for Visual Object Manipulation

Two less addressed issues of deep reinforcement learning are (1) lack of
generalization capability to new target goals, and (2) data inefficiency i.e.,
the model requires several (and often costly) episodes of trial and error to
converge, which makes it impractical to be applied to real-world scenarios. In
this paper, we address these two issues and apply our model to the task of
target-driven visual navigation. To address the first issue, we propose an
actor-critic model whose policy is a function of the goal as well as the
current state, which allows to better generalize. To address the second issue,
we propose AI2-THOR framework, which provides an environment with high-quality
3D scenes and physics engine. Our framework enables agents to take actions and
interact with objects. Hence, we can collect a huge number of training samples
efficiently.
We show that our proposed method (1) converges faster than the
state-of-the-art deep reinforcement learning methods, (2) generalizes across
targets and across scenes, (3) generalizes to a real robot scenario with a
small amount of fine-tuning (although the model is trained in simulation), (4)
is end-to-end trainable and does not need feature engineering, feature matching
between frames or 3D reconstruction of the environment.
The supplementary video can be accessed at the following link:
this https URL

本研究解决深度强化学习的一些问题，如模型推理泛化能力差和数据效率低。提出一种策略演员 - 评论家模型，以目标以及当前状态为函数，并在 AI2-THOR 框架下应用模型以与物体交互，实现在 3D 场景中进行大量样本高效收集，易于应用到现实场景并无需进行特征匹配。