Sandy H. Huang

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Recent work [1], [2] has shown promising results in enabling robotic manipulation of deformable objects through learning from demonstrations. Their method computes a registration from training scene to test scene, and then applies an extrapolation of this registration to the training scene gripper motion to obtain the gripper motion for the test scene. The(More)
Manipulation of deformable objects is a widely applicable but challenging task in robotics. One promising nonparametric approach for this problem is trajectory transfer, in which a non-rigid registration is computed between the starting scene of the demonstration and the scene at test time. This registration is extrapolated to find a function from(More)
Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification. Such adversarial examples have been extensively studied in the context of computer vision applications. In this work, we show adversarial attacks are also effective when targeting neural network policies in reinforcement(More)
We consider the problem of learning from demonstrations to manipulate deformable objects. Recent work [1], [2], [3] has shown promising results that enable robotic manipulation of deformable objects through learning from demonstrations. Their approach is able to generalize from a single demonstration to new test situations, and suggests a nearest neighbor(More)
—Our ultimate goal is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. This behavior is often a direct result of the robot's underlying objective function. Our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do.(More)
Automated manipulation of deformable objects tends to be challenging due to high-dimensional, continuous state-action spaces and due to the complicated dynamics of deformable objects. Direct planning or optimal control techniques are often intractable for this setting. Despite these challenges, recent work [2, 3] has leveraged expert demonstrations to make(More)
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