We present this as a benchmark dataset in noisy learning for video understanding.
We empirically evaluate our approach on multiple different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry.
Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics.
Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.).
Our work is motivated by the intuition that many complex manipulation tasks, with multiple objects, can be simplified by focusing on less complex pairwise object relations.
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e. g., sliding an object to a goal pose while maintaining contact with a table.
In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events.
We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.
Moreover, for the tasks of identifying the important terms in a query and for predicting the additional terms that represent product intent, experiments illustrate that our approaches outperform the non-contextual baselines.
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings.
The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem.
Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets.
In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging.
We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL).
For successful upper limb BCIs, it is important to decode finger movements from brain activity.