Mobile manipulation tasks such as opening a door, pulling open a drawer, or
lifting a toilet lid require constrained motion of the end-effector under
environmental and task constraints. This, coupled with partial information in
novel environments, makes it challenging to employ classical motion planning
approaches at test time. Our key insight is to cast it as a learning problem to
leverage past experience of solving similar planning problems to directly
predict motion plans for mobile manipulation tasks in novel situations at test
time. To enable this, we develop a simulator, ArtObjSim, that simulates
articulated objects placed in real scenes. We then introduce SeqIK+$\theta_0$,
a fast and flexible representation for motion plans. Finally, we learn models
that use SeqIK+$\theta_0$ to quickly predict motion plans for articulating
novel objects at test time. Experimental evaluation shows improved speed and
accuracy at generating motion plans than pure search-based methods and pure
learning methods.

本文介绍了一种将移动机器人操作任务的规划问题转化为学习问题的方法，并提出了一种基于 ArtObjSim 模拟器的快速和灵活的 SeqIK+$	heta_0$ 运动规划表示方法和相应的学习模型，该方法在测试时可以快速准确地预测关节式对象的运动计划。

预测活动普通物品的运动计划

Predicting Motion Plans for Articulating Everyday Objects

An attached arm can significantly increase the applicability of legged robots
to several mobile manipulation tasks that are not possible for the wheeled or
tracked counterparts. The standard hierarchical control pipeline for such
legged manipulators is to decouple the controller into that of manipulation and
locomotion. However, this is ineffective. It requires immense engineering to
support coordination between the arm and legs, and error can propagate across
modules causing non-smooth unnatural motions. It is also biological implausible
given evidence for strong motor synergies across limbs. In this work, we
propose to learn a unified policy for whole-body control of a legged
manipulator using reinforcement learning. We propose Regularized Online
Adaptation to bridge the Sim2Real gap for high-DoF control, and Advantage
Mixing exploiting the causal dependency in the action space to overcome local
minima during training the whole-body system. We also present a simple design
for a low-cost legged manipulator, and find that our unified policy can
demonstrate dynamic and agile behaviors across several task setups. Videos are
at this https URL

提出使用增强学习学习整体控制策略来实现对四肢机器人的控制，以解决四肢协调和模块间错误传播的问题，该统一策略可在多种任务设置中展示动态和敏捷行为。

深度全身控制：学习一体化的操作和运动策略

Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion

Many Reinforcement Learning (RL) approaches use joint control signals
(positions, velocities, torques) as action space for continuous control tasks.
We propose to lift the action space to a higher level in the form of subgoals
for a motion generator (a combination of motion planner and trajectory
executor). We argue that, by lifting the action space and by leveraging
sampling-based motion planners, we can efficiently use RL to solve complex,
long-horizon tasks that could not be solved with existing RL methods in the
original action space. We propose ReLMoGen -- a framework that combines a
learned policy to predict subgoals and a motion generator to plan and execute
the motion needed to reach these subgoals. To validate our method, we apply
ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation
problems where interactions with the environment are required to reach the
destination, and 2) Mobile Manipulation tasks, manipulation tasks that require
moving the robot base. These problems are challenging because they are usually
long-horizon, hard to explore during training, and comprise alternating phases
of navigation and interaction. Our method is benchmarked on a diverse set of
seven robotics tasks in photo-realistic simulation environments. In all
settings, ReLMoGen outperforms state-of-the-art Reinforcement Learning and
Hierarchical Reinforcement Learning baselines. ReLMoGen also shows outstanding
transferability between different motion generators at test time, indicating a
great potential to transfer to real robots.

本文提出了一种名为 ReLMoGen 的 Reinforcement Learning 方法，通过将行动空间提升到更高的阶级形式 —— 子目标，使用基于采样的运动规划器有效解决了传统方法无法解决的长期复杂任务，比如交互式导航和移动机器人操作等。在照片级仿真环境下测试表明，ReLMoGen 在多项机器人任务中表现优异，并具有良好的跨越不同运动生成器的转移能力。

ReLMoGen: 借助动作生成在强化学习中实现移动操纵

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for  Mobile Manipulation

Typical end-to-end formulations for learning robotic navigation involve
predicting a small set of steering command actions (e.g., step forward, turn
left, turn right, etc.) from images of the current state (e.g., a bird's-eye
view of a SLAM reconstruction). Instead, we show that it can be advantageous to
learn with dense action representations defined in the same domain as the
state. In this work, we present "spatial action maps," in which the set of
possible actions is represented by a pixel map (aligned with the input image of
the current state), where each pixel represents a local navigational endpoint
at the corresponding scene location. Using ConvNets to infer spatial action
maps from state images, action predictions are thereby spatially anchored on
local visual features in the scene, enabling significantly faster learning of
complex behaviors for mobile manipulation tasks with reinforcement learning. In
our experiments, we task a robot with pushing objects to a goal location, and
find that policies learned with spatial action maps achieve much better
performance than traditional alternatives.

本文研究机器人导航中的动作表示，提出了 “空间动作地图” 概念，使用卷积神经网络从状态图像推断出空间动作地图，从而显著加快了使用强化学习方法实现移动操作任务的复杂行为的学习