Human pose forecasting is the task of predicting articulated human motion
given past human motion. There exists a number of popular benchmarks that
evaluate an array of different models performing human pose forecasting. These
benchmarks do not reflect that a human interacting system, such as a delivery
robot, observes and plans for the motion of the same individual over an
extended period of time. Every individual has unique and distinct movement
patterns. This is however not reflected in existing benchmarks that evaluate a
model's ability to predict an average human's motion rather than a particular
individual's. We reformulate the human motion forecasting problem and present a
model-agnostic personalization method. Motion forecasting personalization can
be performed efficiently online by utilizing a low-parametric time-series
analysis model that personalizes neural network pose predictions.

我们重新界定了人体动作预测问题，并提出了一种与模型无关的个性化方法，该方法可以通过利用低参数的时间序列分析模型来有效在线实现运动预测个性化。

个性化姿势预测

Personalized Pose Forecasting

How can a delivery robot navigate reliably to a destination in a new office
building, with minimal prior information? To tackle this challenge, this paper
introduces a two-level hierarchical approach, which integrates model-free deep
learning and model-based path planning. At the low level, a neural-network
motion controller, called the intention-net, is trained end-to-end to provide
robust local navigation. The intention-net maps images from a single monocular
camera and "intentions" directly to robot controls. At the high level, a path
planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the
robot's current location to the goal. The planned path provides intentions to
the intention-net. Preliminary experiments suggest that the learned motion
controller is robust against perceptual uncertainty and by integrating with a
path planner, it generalizes effectively to new environments and goals.

该论文介绍了一种基于分层方法的导航系统，该系统采用无模型深度学习和基于模型的路径规划来帮助机器人在新环境下到达目标地点，通过预训练的意图网络来进行本地导航和与路径规划器的融合，实现在新环境和目标中的有效泛化。