Alex Teichman

IROS Conference 2013 Conference Paper

Group induction

• Alex Teichman
• Sebastian Thrun

Machine perception often requires a large amount of user-annotated data which is time-consuming, difficult, or expensive to collect. Perception systems should be easy to train by regular users, and this is currently far from the case. Our previous work, tracking-based semi-supervised learning [14], helped reduce the labeling burden by using tracking information to harvest new and useful training examples. However, [14] was designed for offline use; it assumed a fixed amount of unlabeled data and did not allow for corrections from users. In many practical robot perception scenarios we A) desire continuous learning over a long period of time, B) have a stream of unlabeled sensor data available rather than a fixed dataset, and C) are willing to periodically provide a small number of new training examples. In light of this, we present group induction, a new mathematical framework that rigorously encodes the intuition of [14] in an alternating optimization problem similar to expectation maximization (EM), but with the assumption that the unlabeled data comes in groups of instances that share the same hidden label. The mathematics suggest several improvements to the original heuristic algorithm, and make clear how to handle user interaction and streams of unlabeled data. We evaluate group induction on a track classification task from natural street scenes, demonstrating its ability to learn continuously, adapt to user feedback, and accurately recognize objects of interest.

IROS Conference 2013 Conference Paper

Unsupervised extrinsic calibration of depth sensors in dynamic scenes

• Stephen Miller
• Alex Teichman
• Sebastian Thrun

While inexpensive depth sensors are becoming increasingly ubiquitous, field of view and self-occlusion constraints limit the information a single sensor can provide. For many applications one may instead require a network of depth sensors, registered to a common world frame and synchronized in time. Historically such a setup has required a tedious manual calibration procedure, making it infeasible to deploy these networks in the wild, where spatial and temporal drift are common. In this work, we propose an entirely unsupervised procedure for calibrating the relative pose and time offsets of a pair of depth sensors. So doing, we make no use of an explicit calibration target, or any intentional activity on the part of a user. Rather, we use the unstructured motion of objects in the scene to find potential correspondences between the sensor pair. This yields a rough transform which is then refined with an occlusion-aware energy minimization. We compare our results against the standard checkerboard technique, and provide qualitative examples for scenes in which such a technique would be impossible.

ICRA Conference 2011 Conference Paper

Towards 3D object recognition via classification of arbitrary object tracks

• Alex Teichman
• Jesse Levinson
• Sebastian Thrun

Object recognition is a critical next step for autonomous robots, but a solution to the problem has remained elusive. Prior 3D-sensor-based work largely classifies individual point cloud segments or uses class-specific trackers. In this paper, we take the approach of classifying the tracks of all visible objects. Our new track classification method, based on a mathematically principled method of combining log odds estimators, is fast enough for real time use, is non-specific to object class, and performs well (98. 5% accuracy) on the task of classifying correctly-tracked, well-segmented objects into car, pedestrian, bicyclist, and background classes. We evaluate the classifier's performance using the Stanford Track Collection, a new dataset of about 1. 3 million labeled point clouds in about 14, 000 tracks recorded from an autonomous vehicle research platform. This dataset, which we make publicly available, contains tracks extracted from about one hour of 360-degree, 10Hz depth information recorded both while driving on busy campus streets and parked at busy intersections.

IJCAI Conference 2009 Conference Paper

• Honglak Lee
• Rajat Raina
• Alex Teichman
• Andrew Y. Ng

Sparse coding is an unsupervised learning algorithm for finding concise, slightly higher-level representations of inputs, and has been successfully applied to self-taught learning, where the goal is to use unlabeled data to help on a supervised learning task, even if the unlabeled data cannot be associated with the labels of the supervised task [Raina et al. , 2007]. However, sparse coding uses a Gaussian noise model and a quadratic loss function, and thus performs poorly if applied to binary valued, integer valued, or other non-Gaussian data, such as text. Drawing on ideas from generalized linear models (GLMs), we present a generalization of sparse coding to learning with data drawn from any exponential family distribution (such as Bernoulli, Poisson, etc). This gives a method that we argue is much better suited to model other data types than Gaussian. We present an algorithm for solving the L1regularized optimization problem defined by this model, and show that it is especially efficient when the optimal solution is sparse. We also show that the new model results in significantly improved self-taught learning performance when applied to text classification and to a robotic perception task.