10,109 research outputs found
Physical Primitive Decomposition
Objects are made of parts, each with distinct geometry, physics,
functionality, and affordances. Developing such a distributed, physical,
interpretable representation of objects will facilitate intelligent agents to
better explore and interact with the world. In this paper, we study physical
primitive decomposition---understanding an object through its components, each
with physical and geometric attributes. As annotated data for object parts and
physics are rare, we propose a novel formulation that learns physical
primitives by explaining both an object's appearance and its behaviors in
physical events. Our model performs well on block towers and tools in both
synthetic and real scenarios; we also demonstrate that visual and physical
observations often provide complementary signals. We further present ablation
and behavioral studies to better understand our model and contrast it with
human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
This paper presents a robotic pick-and-place system that is capable of
grasping and recognizing both known and novel objects in cluttered
environments. The key new feature of the system is that it handles a wide range
of object categories without needing any task-specific training data for novel
objects. To achieve this, it first uses a category-agnostic affordance
prediction algorithm to select and execute among four different grasping
primitive behaviors. It then recognizes picked objects with a cross-domain
image classification framework that matches observed images to product images.
Since product images are readily available for a wide range of objects (e.g.,
from the web), the system works out-of-the-box for novel objects without
requiring any additional training data. Exhaustive experimental results
demonstrate that our multi-affordance grasping achieves high success rates for
a wide variety of objects in clutter, and our recognition algorithm achieves
high accuracy for both known and novel grasped objects. The approach was part
of the MIT-Princeton Team system that took 1st place in the stowing task at the
2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
available online at http://arc.cs.princeton.eduComment: Project webpage: http://arc.cs.princeton.edu Summary video:
https://youtu.be/6fG7zwGfIk
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model
Real-time marker-less hand tracking is of increasing importance in
human-computer interaction. Robust and accurate tracking of arbitrary hand
motion is a challenging problem due to the many degrees of freedom, frequent
self-occlusions, fast motions, and uniform skin color. In this paper, we
propose a new approach that tracks the full skeleton motion of the hand from
multiple RGB cameras in real-time. The main contributions include a new
generative tracking method which employs an implicit hand shape representation
based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is
smooth and analytically differentiable making fast gradient based pose
optimization possible. This shape representation, together with a full
perspective projection model, enables more accurate hand modeling than a
related baseline method from literature. Our method achieves better accuracy
than previous methods and runs at 25 fps. We show these improvements both
qualitatively and quantitatively on publicly available datasets.Comment: 8 pages, Accepted version of paper published at 3DV 201
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
- âŠ