Learning latent costs of transitions on graphs from trajectories
demonstrations under various contextual features is challenging but useful for
path planning. Yet, existing methods either oversimplify cost assumptions or
scale poorly with the number of observed trajectories. This paper introduces
DataSP, a differentiable all-to-all shortest path algorithm to facilitate
learning latent costs from trajectories. It allows to learn from a large number
of trajectories in each learning step without additional computation. Complex
latent cost functions from contextual features can be represented in the
algorithm through a neural network approximation. We further propose a method
to sample paths from DataSP in order to reconstruct/mimic observed paths'
distributions. We prove that the inferred distribution follows the maximum
entropy principle. We show that DataSP outperforms state-of-the-art
differentiable combinatorial solver and classical machine learning approaches
in predicting paths on graphs.

从轨迹示范在不同的背景特征下学习图上转换的潜在成本是具有挑战性但有用于路径规划。这篇论文引入了 DataSP，它是一种可微分的全对全最短路径算法，用于从轨迹中学习潜在成本。通过神经网络近似，可以在算法中表达出来自背景特征的复杂潜在成本函数。我们进一步提出了一种从 DataSP 中采样路径的方法，以重构或模仿观测到的路径分布。我们证明了推断出的分布遵循最大熵原理。我们展示了 DataSP 在预测图上的路径时优于现有最新的可微组合求解器和经典机器学习方法。

DataSP：一种基于上下文的差分全互相最短路径算法用于学习成本和预测路径

DataSP: A Differential All-to-All Shortest Path Algorithm for Learning  Costs and Predicting Paths with Context

In-betweening is a technique for generating transitions given initial and
target character states. The majority of existing works require multiple (often
$>$10) frames as input, which are not always accessible. Our work deals with a
focused yet challenging problem: to generate the transition when given exactly
two frames (only the first and last). To cope with this challenging scenario,
we implement our bi-directional scheme which generates forward and backward
transitions from the start and end frames with two adversarial autoregressive
networks, and stitches them in the middle of the transition where there is no
strict ground truth. The autoregressive networks based on conditional
variational autoencoders (CVAE) are optimized by searching for a pair of
optimal latent codes that minimize a novel stitching loss between their
outputs. Results show that our method achieves higher motion quality and more
diverse results than existing methods on both the LaFAN1 and Human3.6m
datasets.

使用基于条件变分自动编码器的自回归网络实现单两帧生成过渡，结果表明其在动作质量和多样性方面均比现有方法更加优秀。

双姿态拼接的多样化动画中间帧生成

Diverse Motion In-betweening with Dual Posture Stitching

Gaits and transitions are key components in legged locomotion. For legged
robots, describing and reproducing gaits as well as transitions remain
longstanding challenges. Reinforcement learning has become a powerful tool to
formulate controllers for legged robots. Learning multiple gaits and
transitions, nevertheless, is related to the multi-task learning problems. In
this work, we present a novel framework for training a simple control policy
for a quadruped robot to locomote in various gaits. Four independent phases are
used as the interface between the gait generator and the control policy, which
characterizes the movement of four feet. Guided by the phases, the quadruped
robot is able to locomote according to the generated gaits, such as walk, trot,
pacing and bounding, and to make transitions among those gaits. More general
phases can be used to generate complex gaits, such as mixed rhythmic dancing.
With the control policy, the Black Panther robot, a medium-dog-sized quadruped
robot, can perform all learned motor skills while following the velocity
commands smoothly and robustly in natural environment.

本文提出了学习控制犬形四足机器人在各种步态下运动的框架，并通过强化学习算法解决了多任务学习的问题。使用四个独立的相位来描述步态发生器和控制策略之间的接口，使用该控制策略，黑豹四足机器人可以在自然环境下流畅、稳健地学习所有运动技能并遵循速度命令进行运动。