PhD Speaking Qualifier

Abstract: Owing to the curse of dimensionality, numerically computing global policies to optimal control problems for complex dynamical systems quickly becomes intractable. In consequence, a number of approximation methods have been developed. However, none of the current methods can quantify by how much the resulting control underperforms the elusive globally optimal solution. We propose Policy [...]

Abstract: Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics. In particular, the method of Maximum Causal Entropy IRL is based on the perspective of entropy maximization, while related advances in the field of economics instead assume the existence of unobserved action shocks to explain [...]

Abstract: To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. It is often infeasible to train monolithic neural network policies across such large variance in object properties. Towards this generalization challenge, we propose task-axis controllers, which are defined relative to [...]

Abstract: Real-world environments, such as homes, hospitals, and restaurants, often contain many objects that a robot could possibly manipulate. However, for a given manipulation task, only a small number of objects and object properties may actually be relevant. This talk presents CREST (Causal Reasoning for Efficient Structure Transfer), our approach to learn the relevant state [...]

Abstract: A large body of research exists on grasping for objects with ideal properties like Lambertian reflectance and rigidity. On the other hand, real-world environments contain many objects for which such properties do not hold, such as transparent, specular, and deformable objects. For such objects, new approaches are required to achieve the same level of [...]