26 research outputs found
Accelerating Random Kaczmarz Algorithm Based on Clustering Information
Kaczmarz algorithm is an efficient iterative algorithm to solve
overdetermined consistent system of linear equations. During each updating
step, Kaczmarz chooses a hyperplane based on an individual equation and
projects the current estimate for the exact solution onto that space to get a
new estimate. Many vairants of Kaczmarz algorithms are proposed on how to
choose better hyperplanes. Using the property of randomly sampled data in
high-dimensional space, we propose an accelerated algorithm based on clustering
information to improve block Kaczmarz and Kaczmarz via Johnson-Lindenstrauss
lemma. Additionally, we theoretically demonstrate convergence improvement on
block Kaczmarz algorithm
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Articulated object manipulation is a fundamental yet challenging task in
robotics. Due to significant geometric and semantic variations across object
categories, previous manipulation models struggle to generalize to novel
categories. Few-shot learning is a promising solution for alleviating this
issue by allowing robots to perform a few interactions with unseen objects.
However, extant approaches often necessitate costly and inefficient test-time
interactions with each unseen instance. Recognizing this limitation, we observe
that despite their distinct shapes, different categories often share similar
local geometries essential for manipulation, such as pullable handles and
graspable edges - a factor typically underutilized in previous few-shot
learning works. To harness this commonality, we introduce 'Where2Explore', an
affordance learning framework that effectively explores novel categories with
minimal interactions on a limited number of instances. Our framework explicitly
estimates the geometric similarity across different categories, identifying
local areas that differ from shapes in the training categories for efficient
exploration while concurrently transferring affordance knowledge to similar
parts of the objects. Extensive experiments in simulated and real-world
environments demonstrate our framework's capacity for efficient few-shot
exploration and generalization
DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry
3D shape generation is a fundamental operation in computer graphics. While
significant progress has been made, especially with recent deep generative
models, it remains a challenge to synthesize high-quality geometric shapes with
rich detail and complex structure, in a controllable manner. To tackle this, we
introduce DSM-Net, a deep neural network that learns a disentangled structured
mesh representation for 3D shapes, where two key aspects of shapes, geometry
and structure, are encoded in a synergistic manner to ensure plausibility of
the generated shapes, while also being disentangled as much as possible. This
supports a range of novel shape generation applications with intuitive control,
such as interpolation of structure (geometry) while keeping geometry
(structure) unchanged. To achieve this, we simultaneously learn structure and
geometry through variational autoencoders (VAEs) in a hierarchical manner for
both, with bijective mappings at each level. In this manner we effectively
encode geometry and structure in separate latent spaces, while ensuring their
compatibility: the structure is used to guide the geometry and vice versa. At
the leaf level, the part geometry is represented using a conditional part VAE,
to encode high-quality geometric details, guided by the structure context as
the condition. Our method not only supports controllable generation
applications, but also produces high-quality synthesized shapes, outperforming
state-of-the-art methods
COPILOT: Human Collision Prediction and Localization from Multi-view Egocentric Videos
To produce safe human motions, assistive wearable exoskeletons must be
equipped with a perception system that enables anticipating potential
collisions from egocentric observations. However, previous approaches to
exoskeleton perception greatly simplify the problem to specific types of
environments, limiting their scalability. In this paper, we propose the
challenging and novel problem of predicting human-scene collisions for diverse
environments from multi-view egocentric RGB videos captured from an
exoskeleton. By classifying which body joints will collide with the environment
and predicting a collision region heatmap that localizes potential collisions
in the environment, we aim to develop an exoskeleton perception system that
generalizes to complex real-world scenes and provides actionable outputs for
downstream control. We propose COPILOT, a video transformer-based model that
performs both collision prediction and localization simultaneously, leveraging
multi-view video inputs via a proposed joint space-time-viewpoint attention
operation. To train and evaluate the model, we build a synthetic data
generation framework to simulate virtual humans moving in photo-realistic 3D
environments. This framework is then used to establish a dataset consisting of
8.6M egocentric RGBD frames to enable future work on the problem. Extensive
experiments suggest that our model achieves promising performance and
generalizes to unseen scenes as well as real world. We apply COPILOT to a
downstream collision avoidance task, and successfully reduce collision cases by
29% on unseen scenes using a simple closed-loop control algorithm.Comment: 8 pages, 6 figure