We present a series of experiments which explore the use of consumer-grade accelerometers as joint position sensors for robotic manipulators. We show that 6- and 7-dof joint angle estimation is possible by using one 3-d accelerometer for each pair of joints. We demonstrate two calibration approaches and experimental results using accelerometer-based control in both position-control […]
We consider the task of grasping novel objects and cleaning fairly cluttered tables with many novel objects. Recent successful approaches employ machine learning algorithms to identify points on the scene that the robot should grasp. In this paper, we show that the task can be significantly simplified by using segmentation, especially with depth information. A […]
We consider the task of accurately controlling a complex system, such as autonomously sliding a car sideways into a parking spot. Although certain regions of this domain are extremely hard to model (i.e., the dynamics of the car while skidding), we observe that in practice such systems are often remarkably deterministic over short periods of […]
Robust object detection is a critical skill for robotic applications in complex environments like homes and offices. In this paper we propose a method for using multiple cameras to simultaneously view an object from multiple angles and at high resolutions. We show that our probabilistic method for combining the camera views, which can be used […]
We consider the problem of grasping novel objects and its application to cleaning a desk. A recent successful approach applies machine learning to learn one grasp point in an image and a point cloud. Although those methods are able to generalize to novel objects, they yield suboptimal results because they rely on motion planner for […]
Although robot navigation in indoor environments has achieved great success, robots are unable to fully navigate these spaces without the ability to operate elevators, including those which the robot has not seen before. In this paper, we focus on the key challenge of autonomous interaction with an unknown elevator button panel. A number of factors, […]
Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopter’s capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks being […]
Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having devicelevel energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in […]
Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture. In this paper, we consider the problem of learning invariances, rather than […]
Vector representations of words capture relationships in words’ functions and meanings. Many existing techniques for inducing such representations from data use a pipeline of hand-coded processing techniques. Neural language models offer principled techniques to learn word vectors using a probabilistic modeling approach. However, learning word vectors via language modeling produces representations with a syntactic focus, […]
Natural language parsing has typically been done with small sets of discrete categories such as NP and VP, but this representation does not capture the full syntactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases only partly address the problem at the cost of huge feature spaces and […]
We consider the problem of automatically col- lecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules. In particular, we describe a system by which a SLAM- generated map of an office environment can be annotated with text labels such as room numbers and the names of […]
