Tensegrity robots, composed of rigid rods and flexible cables, are difficult
to accurately model and control given the presence of complex dynamics and high
number of DoFs. Differentiable physics engines have been recently proposed as a
data-driven approach for model identification of such complex robotic systems.
These engines are often executed at a high-frequency to achieve accurate
simulation. Ground truth trajectories for training differentiable engines,
however, are not typically available at such high frequencies due to
limitations of real-world sensors. The present work focuses on this frequency
mismatch, which impacts the modeling accuracy. We proposed a recurrent
structure for a differentiable physics engine of tensegrity robots, which can
be trained effectively even with low-frequency trajectories. To train this new
recurrent engine in a robust way, this work introduces relative to prior work:
(i) a new implicit integration scheme, (ii) a progressive training pipeline,
and (iii) a differentiable collision checker. A model of NASA's icosahedron
SUPERballBot on MuJoCo is used as the ground truth system to collect training
data. Simulated experiments show that once the recurrent differentiable engine
has been trained given the low-frequency trajectories from MuJoCo, it is able
to match the behavior of MuJoCo's system. The criterion for success is whether
a locomotion strategy learned using the differentiable engine can be
transferred back to the ground-truth system and result in a similar motion.
Notably, the amount of ground truth data needed to train the differentiable
engine, such that the policy is transferable to the ground truth system, is 1%
of the data needed to train the policy directly on the ground-truth system.

本研究提出一种适用于低频轨迹的递归不同 iable 物理引擎，该引擎的训练使用了 NASA 的超级球形机器人的 MuJoCo 模型，实验证明该引擎只需原系统训练数据的 1％即可将学习策略转移到原系统并成功进行类似的运动。

可微分重复引擎模拟张力机器人，适用于低频数据训练

A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data

A recurrent structure is a popular framework choice for the task of video
super-resolution. The state-of-the-art method BasicVSR adopts bidirectional
propagation with feature alignment to effectively exploit information from the
entire input video. In this study, we redesign BasicVSR by proposing
second-order grid propagation and flow-guided deformable alignment. We show
that by empowering the recurrent framework with the enhanced propagation and
alignment, one can exploit spatiotemporal information across misaligned video
frames more effectively. The new components lead to an improved performance
under a similar computational constraint. In particular, our model BasicVSR++
surpasses BasicVSR by 0.82 dB in PSNR with similar number of parameters. In
addition to video super-resolution, BasicVSR++ generalizes well to other video
restoration tasks such as compressed video enhancement. In NTIRE 2021,
BasicVSR++ obtains three champions and one runner-up in the Video
Super-Resolution and Compressed Video Enhancement Challenges. Codes and models
will be released to MMEditing.

通过提出二阶网格传递和流引导可变对齐的方法重新设计 BasicVSR，强化循环框架，更有效地利用错位视频帧中的时空信息和对齐，使模型更好地推广到其他视频恢复任务中，如压缩视频增强。在类似的计算限制下，BasicVSR++ 的 PSNR 比 BasicVSR 高 0.82db。

BasicVSR++：通过增强传播和对齐提高视频超分辨率

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation  and Alignment

We review Boltzmann machines extended for time-series. These models often
have recurrent structure, and back propagration through time (BPTT) is used to
learn their parameters. The per-step computational complexity of BPTT in online
learning, however, grows linearly with respect to the length of preceding
time-series (i.e., learning rule is not local in time), which limits the
applicability of BPTT in online learning. We then review dynamic Boltzmann
machines (DyBMs), whose learning rule is local in time. DyBM's learning rule
relates to spike-timing dependent plasticity (STDP), which has been postulated
and experimentally confirmed for biological neural networks.

本文综述了针对时间序列扩展的玻尔兹曼机，介绍了后向传递算法通过时间学习参数的实现。此外，为了解决在线学习中 BPTT 的计算复杂度随着时间序列长度的线性增长的问题，介绍了动态玻尔兹曼机（DyBM）及其基于时间本地性的学习规则，该规则与生物神经网络中的时序相关可塑性（STDP）有关，并被实验证实。