We present Dynamics-Guided Diffusion Model, a data-driven framework for
generating manipulator geometry designs for a given manipulation task. Instead
of training different design models for each task, our approach employs a
learned dynamics network shared across tasks. For a new manipulation task, we
first decompose it into a collection of individual motion targets which we call
target interaction profile, where each individual motion can be modeled by the
shared dynamics network. The design objective constructed from the target and
predicted interaction profiles provides a gradient to guide the refinement of
finger geometry for the task. This refinement process is executed as a
classifier-guided diffusion process, where the design objective acts as the
classifier guidance. We evaluate our framework on various manipulation tasks,
under the sensor-less setting using only an open-loop parallel jaw motion. Our
generated designs outperform optimization-based and unguided diffusion
baselines relatively by 31.5% and 45.3% on average manipulation success rate.
With the ability to generate a design within 0.8 seconds, our framework could
facilitate rapid design iteration and enhance the adoption of data-driven
approaches for robotic mechanism design.

通过 Dynamics-Guided Diffusion Model 生成操纵性机器人的几何设计，利用目标交互配置来指导设计优化过程以及加强数据驱动方法在机器人结构设计中的应用。

机器人机械臂设计中的动力学指导扩散模型

Dynamics-Guided Diffusion Model for Robot Manipulator Design

We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning
boost for pre-trained MIM models. The motivation behind MIM-Refiner is rooted
in the insight that optimal representations within MIM models generally reside
in intermediate layers. Accordingly, MIM-Refiner leverages multiple contrastive
heads that are connected to diverse intermediate layers. In each head, a
modified nearest neighbor objective helps to construct respective semantic
clusters.
The refinement process is short but effective. Within a few epochs, we refine
the features of MIM models from subpar to state-of-the-art, off-the-shelf
features. Refining a ViT-H, pre-trained with data2vec 2.0 on ImageNet-1K,
achieves new state-of-the-art results in linear probing (84.7%) and low-shot
classification among models that are pre-trained on ImageNet-1K. In ImageNet-1K
1-shot classification, MIM-Refiner sets a new state-of-the-art of 64.2%,
outperforming larger models that were trained on up to 2000x more data such as
DINOv2-g, OpenCLIP-G and MAWS-6.5B. Project page:
this https URL

MIM-Refiner 通过对比学习提升预训练的 MIM 模型，并在几个时代内将其从次优到最先进的特征状态进行了有效的精炼，该方法在线性探测和低样本分类中取得了新的最先进结果。

MIM-Refiner：中间层预训练表征的对比学习强化

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained  Representations

Difference features obtained by comparing the images of two periods play an
indispensable role in the change detection (CD) task. However, a pair of
bi-temporal images can exhibit diverse changes, which may cause various
difference features. Identifying changed pixels with differ difference features
to be the same category is thus a challenge for CD. Most nowadays' methods
acquire distinctive difference features in implicit ways like enhancing image
representation or supervision information. Nevertheless, informative image
features only guarantee object semantics are modeled and can not guarantee that
changed pixels have similar semantics in the difference feature space and are
distinct from those unchanged ones. In this work, the generalized
representation of various changes is learned straightforwardly in the
difference feature space, and a novel Changes-Aware Transformer (CAT) for
refining difference features is proposed. This generalized representation can
perceive which pixels are changed and which are unchanged and further guide the
update of pixels' difference features. CAT effectively accomplishes this
refinement process through the stacked cosine cross-attention layer and
self-attention layer. After refinement, the changed pixels in the difference
feature space are closer to each other, which facilitates change detection. In
addition, CAT is compatible with various backbone networks and existing CD
methods. Experiments on remote sensing CD data set and street scene CD data set
show that our method achieves state-of-the-art performance and has excellent
generalization.

学习差异特征空间中各种变化的广义表示，提出了一种新颖的具有改善差异特征的 Changes-Aware Transformer (CAT)。CAT 通过堆叠的余弦交叉注意力层和自注意力层有效地完成了这一改进过程，进而使差异特征空间中的变化像素更接近，从而有利于变化检测。