Virtualizing the physical world into virtual models has been a critical
technique for robot navigation and planning in the real world. To foster
manipulation with articulated objects in everyday life, this work explores
building articulation models of indoor scenes through a robot's purposeful
interactions in these scenes. Prior work on articulation reasoning primarily
focuses on siloed objects of limited categories. To extend to room-scale
environments, the robot has to efficiently and effectively explore a
large-scale 3D space, locate articulated objects, and infer their
articulations. We introduce an interactive perception approach to this task.
Our approach, named Ditto in the House, discovers possible articulated objects
through affordance prediction, interacts with these objects to produce
articulated motions, and infers the articulation properties from the visual
observations before and after each interaction. It tightly couples affordance
prediction and articulation inference to improve both tasks. We demonstrate the
effectiveness of our approach in both simulation and real-world scenes. Code
and additional results are available at
this https URL

为了在日常生活中与可动物体实现互动，本文探索了通过机器人在室内场景中有目的地交互以建立室内场景的可动模型的方法，并引入了一个名为 Ditto in the House 的交互感知方法，通过可行性预测发现可能的可动物体，与这些物体交互以产生可动运动，并从每次交互前后的视觉观察中推断出其可动性质。

室内模型的交互感知式建模

Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception

Object finding in clutter is a skill that requires perception of the
environment and in many cases physical interaction. In robotics, interactive
perception defines a set of algorithms that leverage actions to improve the
perception of the environment, and vice versa use perception to guide the next
action. Scene interactions are difficult to model, therefore, most of the
current systems use predefined heuristics. This limits their ability to
efficiently search for the target object in a complex environment. In order to
remove heuristics and the need for explicit models of the interactions, in this
work we propose a reinforcement learning based active and interactive
perception system for scene exploration and object search. We evaluate our work
both in simulated and in real-world experiments using a robotic manipulator
equipped with an RGB and a depth camera, and compare our system to two
baselines. The results indicate that our approach, trained in simulation only,
transfers smoothly to reality and can solve the object finding task efficiently
and with more than 88% success rate.

研究了基于强化学习的互动感知系统在场景探索和物体搜索方面的应用，结果表明该方法不再需要预定义的启发式模型，可在模拟和现实世界中有效地解决这一任务。

利用交互感知在杂乱场景中找到物品

Object Finding in Cluttered Scenes Using Interactive Perception

Recent approaches in robotics follow the insight that perception is
facilitated by interaction with the environment. These approaches are subsumed
under the term of Interactive Perception (IP). It provides the following
benefits: (i) interaction with the environment creates a rich sensory signal
that would otherwise not be present and (ii) knowledge of the regularity in the
combined space of sensory data and action parameters facilitate the prediction
and interpretation of the signal. In this survey we postulate this as a
principle and collect evidence in support by analyzing and categorizing
existing work in this area. We also provide an overview of the most important
applications of Interactive Perception. We close this survey by discussing
remaining open questions. Thereby, we hope to define a field and inspire future
work.

探讨交互感知在机器人领域的原理和应用，强调交互作用可以产生更丰富的感知信号，并且感官数据和运动参数的组合空间中的规律性知识可以促进预测和解释信号，同时分析现有工作，并提出未来需要解决的问题。