Dense packing in pick-and-place systems is an important feature in many
warehouse and logistics applications. Prior work in this space has largely
focused on planning algorithms in simulation, but real-world packing
performance is often bottlenecked by the difficulty of perceiving 3D object
geometry in highly occluded, partially observed scenes. In this work, we
present a fully-convolutional shape completion model, F-CON, which can be
easily combined with off-the-shelf planning methods for dense packing in the
real world. We also release a simulated dataset, COB-3D-v2, that can be used to
train shape completion models for real-word robotics applications, and use it
to demonstrate that F-CON outperforms other state-of-the-art shape completion
methods. Finally, we equip a real-world pick-and-place system with F-CON, and
demonstrate dense packing of complex, unseen objects in cluttered scenes.
Across multiple planning methods, F-CON enables substantially better dense
packing than other shape completion methods.

通过与现有规划方法结合，我们提出了一种全卷积形状补全模型（F-CON），可以在现实世界中实现密集堆放。我们还发布了一个模拟数据集（COB-3D-v2），用于训练真实世界机器人应用的形状补全模型，并使用它证明 F-CON 超过其他最先进的形状补全方法。最后，我们将 F-CON 应用于实际的拾取放置系统，并展示了在混乱的场景中对复杂、未知物体的密集堆放。在多种规划方法下，F-CON 实现了比其他形状补全方法更好的密集堆放效果。

用于复杂、新颖物体的稠密填充的卷积占空模型

Convolutional Occupancy Models for Dense Packing of Complex, Novel  Objects

For real-world applications, robots will need to continually learn in their
environments through limited interactions with their users. Toward this,
previous works in few-shot class incremental learning (FSCIL) and active class
selection (ACS) have achieved promising results but were tested in constrained
setups. Therefore, in this paper, we combine ideas from FSCIL and ACS to
develop a novel framework that can allow an autonomous agent to continually
learn new objects by asking its users to label only a few of the most
informative objects in the environment. To this end, we build on a
state-of-the-art (SOTA) FSCIL model and extend it with techniques from ACS
literature. We term this model Few-shot Incremental Active class SeleCtiOn
(FIASco). We further integrate a potential field-based navigation technique
with our model to develop a complete framework that can allow an agent to
process and reason on its sensory data through the FIASco model, navigate
towards the most informative object in the environment, gather data about the
object through its sensors and incrementally update the FIASco model.
Experimental results on a simulated agent and a real robot show the
significance of our approach for long-term real-world robotics applications.

通过与用户的有限交互，为真实世界的应用而设计的机器人将需要不断学习其环境。本文结合了少样本类增量学习（FSCIL）和主动类选择（ACS）的思想，开发了一个新框架，使自主代理能够通过要求用户仅对环境中最富信息的少数对象进行标记来持续学习新对象。我们构建并扩展了一种最先进的 FSCIL 模型，并结合 ACS 文献中的技术，将其命名为少样本增量主动类选择（FIASco）模型。我们进一步将基于潜在场的导航技术与我们的模型集成，开发了一个完整的框架，可以使代理通过 FIASco 模型对其感知数据进行处理和推理，朝向环境中最富信息的对象导航，通过其传感器收集对象的数据，并逐步更新 FIASco 模型。在模拟代理和真实机器人上的实验结果显示了我们的方法对于长期的真实世界机器人应用的重要性。

少样本类别增量学习的主动类别选择

Active Class Selection for Few-Shot Class-Incremental Learning

In this paper, we propose Multi-View Dreaming, a novel reinforcement learning
agent for integrated recognition and control from multi-view observations by
extending Dreaming. Most current reinforcement learning method assumes a
single-view observation space, and this imposes limitations on the observed
data, such as lack of spatial information and occlusions. This makes obtaining
ideal observational information from the environment difficult and is a
bottleneck for real-world robotics applications. In this paper, we use
contrastive learning to train a shared latent space between different
viewpoints, and show how the Products of Experts approach can be used to
integrate and control the probability distributions of latent states for
multiple viewpoints. We also propose Multi-View DreamingV2, a variant of
Multi-View Dreaming that uses a categorical distribution to model the latent
state instead of the Gaussian distribution. Experiments show that the proposed
method outperforms simple extensions of existing methods in a realistic robot
control task.

本文提出了一个新的强化学习代理，Multi-View Dreaming，用于从多视角观测中进行综合识别和控制。我们使用对比学习来训练不同视角之间的共享潜在空间，并展示了如何使用专家产品方法来集成和控制多个视角的概率分布的潜在状态。我们还提出了 Multi-View DreamingV2，它使用分类分布来建模潜在状态，并证明该方法在实际机器人控制任务中优于现有方法的简单扩展。