We present our vision-based system for grasping novel objects in clut- tered environments. Our system can be divided into four components: 1) decide where to grasp an object, 2) perceive obstacles, 3) plan an obstacle-free path, and 4) follow the path to grasp the object. While most prior work assumes availability of a detailed 3-d […]
We consider the task of creating a 3-d model of a large novel environment, given only a small number of images of the scene. This is a difficult problem, because if the images are taken from very different viewpoints or if they contain similar-looking structures, then most geometric reconstruction methods will have great difficulty finding […]
We consider the problem of estimating detailed 3-d structure from a single still image of an unstructured environment. Our goal is to create 3-d models which are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (MRF) to infer a set of […]
We consider the problem of grasping novel objects, specifically ones that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is not available, is a challenging problem. Further, even if given a model, one still has to decide where to grasp the object. We […]
Legged robots have the potential to navigate a much larger variety of terrain than their wheeled counterparts. In this paper we present a hierarchical control architecture that enables a quadruped, the “LittleDog” robot, to walk over rough terrain. The controller consists of a high-level planner that plans a set of footsteps across the terrain, a […]
We consider the problem of grasping novel objects in cluttered environments. If a full 3-d model of the scene were available, one could use the model to estimate the stability and robustness of different grasps (formalized as form/force-closure, etc); in practice, however, a robot facing a novel object will usually be able to perceive only […]
When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or failure of the system. However, it is typically extremely laborintensive to collect data, as the process usually involves acquiring the image, then manual cropping and hand-labeling. Preparing large […]
Humans have an amazing ability to perceive depth from a single still image; however, it remains a challenging problem for current computer vision systems. In this paper, we will present algorithms for estimating depth from a single still image. There are numerous monocular cues—such as texture variations and gradients, defocus, color/haze, etc.—that can be used […]
As robots enter novel, uncertain home and office environments, they are able to navigate these environments successfully. However, to be practically deployed, robots should be able to manipulate their environment to gain access to new spaces, such as by opening a door and operating an elevator. This, however, remains a challenging problem because a robot […]
Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon’s Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing […]
We consider the problem of estimating detailed 3-d structure from a single still image of an unstructured environment. Our goal is to create 3-d models which are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (MRF) to infer a set of […]
The problem of object detection and recognition is a notori- ously difficult one, and one that has been the focus of much work in the computer vision and robotics communities. Most work has concentrated on systems that operate purely on visual inputs (i.e., images) and largely ignores other sensor modalities. However, despite the great progress […]
