Human-scene Interaction (HSI) generation is a challenging task and crucial
for various downstream tasks. However, one of the major obstacles is the
limited data scale. High-quality data with simultaneously captured human and 3D
environments is rare, resulting in limited data diversity and complexity. In
this work, we argue that interaction with a scene is essentially interacting
with the space occupancy of the scene from an abstract physical perspective,
leading us to a unified novel view of Human-Occupancy Interaction. By treating
pure motion sequences as records of humans interacting with invisible scene
occupancy, we can aggregate motion-only data into a large-scale paired
human-occupancy interaction database: Motion Occupancy Base (MOB). Thus, the
need for costly paired motion-scene datasets with high-quality scene scans can
be substantially alleviated. With this new unified view of Human-Occupancy
interaction, a single motion controller is proposed to reach the target state
given the surrounding occupancy. Once trained on MOB with complex occupancy
layout, the controller could handle cramped scenes and generalize well to
general scenes with limited complexity. With no GT 3D scenes for training, our
method can generate realistic and stable HSI motions in diverse scenarios,
including both static and dynamic scenes. Our code and data would be made
publicly available at this https URL

人 - 场景交互生成是一项具有挑战性且关键的任务，但是受到有限的数据规模的限制。通过将纯运动序列视作与不可见场景占用交互的记录，利用大规模配对的数据集 Motion Occupancy Base（MOB），我们提出了一种新的人 - 占有交互的统一视角。在 MOB 上训练的单个运动控制器可以处理狭窄的场景，并在复杂度有限的一般场景中具有良好的泛化性。我们的方法可以在各种场景中生成逼真且稳定的人 - 场景交互动作。

重新审视人 - 场景交互的空间占用

Revisit Human-Scene Interaction via Space Occupancy

The following is a user guide for the Dual Radar SAR Controller graphical
user interface (GUI) to operate the dual radar synthetic aperture radar (SAR)
scanner. The scanner was designed in the Spring semester of 2022 by Josiah
Smith (RA), Yusef Alimam (UG), and Geetika Vedula (UG) with multiple axes of
motion for the radar and target under test. The system is operated by a
personal computer (PC) running MATLAB. An AMC4030 motion controller is employed
to control the mechanical system. An ESP32 microcontroller synchronizes the
mechanical motion and radar frame firing to achieving precise positioning at
high movement speeds; the software was designed by Josiah Smith (RA) and
Benjamin Roy (UG). A second system is designed that employs 3-axes of motion
(X-Y + rotation) for fine control over the location of the target under test.
The entire system is capable of efficiently collecting data from colocated and
non-colocated radars for multiband fusion imaging in addition to simple single
radar imaging.

该研究论文介绍了使用双雷达合成孔径雷达扫描仪的双雷达 SAR 控制器图形用户界面（GUI）的用户指南，该系统可进行多带融合成像以及简单的单雷达成像。

双雷达合成孔径雷达控制器

Dual Radar SAR Controller

In this paper, we present a data-driven strategy to simplify the deployment
of model-based controllers in legged robotic hardware platforms. Our approach
leverages a model-free safe learning algorithm to automate the tuning of
control gains, addressing the mismatch between the simplified model used in the
control formulation and the real system. This method substantially mitigates
the risk of hazardous interactions with the robot by sample-efficiently
optimizing parameters within a probably safe region. Additionally, we extend
the applicability of our approach to incorporate the different gait parameters
as contexts, leading to a safe, sample-efficient exploration algorithm capable
of tuning a motion controller for diverse gait patterns. We validate our method
through simulation and hardware experiments, where we demonstrate that the
algorithm obtains superior performance on tuning a model-based motion
controller for multiple gaits safely.

本文提出了一种数据驱动的策略，通过利用模型无关的安全学习算法，自动调整控制增益来简化机器人硬件平台中基于模型的控制器的部署，从而解决了控制制定中所使用的简化模型与实际系统之间的不匹配问题，并通过模拟和硬件实验验证了该方法的有效性。