We introduce the Riemannian Motion Policy (RMP), a new mathematical object
for modular motion generation. An RMP is a second-order dynamical system
(acceleration field or motion policy) coupled with a corresponding Riemannian
metric. The motion policy maps positions and velocities to accelerations, while
the metric captures the directions in the space important to the policy. We
show that RMPs provide a straightforward and convenient method for combining
multiple motion policies and transforming such policies from one space (such as
the task space) to another (such as the configuration space) in geometrically
consistent ways. The operators we derive for these combinations and
transformations are provably optimal, have linearity properties making them
agnostic to the order of application, and are strongly analogous to the
covariant transformations of natural gradients popular in the machine learning
literature. The RMP framework enables the fusion of motion policies from
different motion generation paradigms, such as dynamical systems, dynamic
movement primitives (DMPs), optimal control, operational space control,
nonlinear reactive controllers, motion optimization, and model predictive
control (MPC), thus unifying these disparate techniques from the literature.
RMPs are easy to implement and manipulate, facilitate controller design,
simplify handling of joint limits, and clarify a number of open questions
regarding the proper fusion of motion generation methods (such as incorporating
local reactive policies into long-horizon optimizers). We demonstrate the
effectiveness of RMPs on both simulation and real robots, including their
ability to naturally and efficiently solve complicated collision avoidance
problems previously handled by more complex planners.

该研究介绍了黎曼运动策略 (RMP) 的概念，它是一个二阶动态系统和黎曼度量的组合体，可用于集成多个运动策略并在不同空间中进行转换。RMP 架构易于实现和操作，有助于控制器设计，简化关节极限的处理，并通过模拟和机器人实验验证了其有效性。

黎曼运动策略

Riemannian Motion Policies

Visual recognition systems mounted on autonomous moving agents face the
challenge of unconstrained data, but simultaneously have the opportunity to
improve their performance by moving to acquire new views of test data. In this
work, we first show how a recurrent neural network-based system may be trained
to perform end-to-end learning of motion policies suited for this "active
recognition" setting. Further, we hypothesize that active vision requires an
agent to have the capacity to reason about the effects of its motions on its
view of the world. To verify this hypothesis, we attempt to induce this
capacity in our active recognition pipeline, by simultaneously learning to
forecast the effects of the agent's motions on its internal representation of
the environment conditional on all past views. Results across two challenging
datasets confirm both that our end-to-end system successfully learns meaningful
policies for active category recognition, and that "learning to look ahead"
further boosts recognition performance.

通过训练循环神经网络模型的运动策略，结合学习预测代理在移动中对环境的影响，我们成功实现了主动学习视觉识别，并提高了识别性能。