arXiv subjects

Mohit Sharma

Publications and source records attributed to Mohit Sharma.

53 records · Page 3Linked to original sources

arXiv2020

Distant-Supervised Slot-Filling for E-Commerce Queries

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.). These characteristics can then be used by a search engine to return results that better match the query's product intent. Traditional methods for slot-filling require the availability of training data with ground truth slot-annotation information. However, generating such labeled data, especially in e-commerce is expensive and time-consuming because the number of slots increases as new products are added. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical query logs and the purchases that these queries led to, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering how they affect retrieval performance, as well as how well they classify the slots. In terms of retrieval, our approaches achieve better ranking performance (up to 156%) over Okapi BM25. Moreover, our approach that leverages co-occurrence information leads to better performance than the one that does not on both the retrieval and slot classification tasks.

cs.IR↗

arXiv2020

Relational Learning for Skill Preconditions

To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured environments, precondition models will need to generalize to variable number of objects with different shapes and sizes. In this work, we focus on learning precondition models for manipulation skills in unconstrained environments. 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. We propose an object-relation model that learns continuous representations for these pairwise object relations. Our object-relation model is trained completely in simulation, and once learned, is used by a separate precondition model to predict skill preconditions for real world tasks. We evaluate our precondition model on $3$ different manipulation tasks: sweeping, cutting, and unstacking. We show that our approach leads to significant improvements in predicting preconditions for all 3 tasks, across objects of different shapes and sizes.

cs.RO↗

arXiv2020

Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation

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. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer without fine-tuning.

cs.RO↗

arXiv2020

A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy

We designed a modular robotic control stack that provides a customizable and accessible interface to the Franka Emika Panda Research robot. This framework abstracts high-level robot control commands as skills, which are decomposed into combinations of trajectory generators, feedback controllers, and termination handlers. Low-level control is implemented in C++ and runs at $1$kHz, and high-level commands are exposed in Python. In addition, external sensor feedback, like estimated object poses, can be streamed to the low-level controllers in real time. This modular approach allows us to quickly prototype new control methods, which is essential for research applications. We have applied this framework across a variety of real-world robot tasks in more than $5$ published research papers. The framework is currently shared internally with other robotics labs at Carnegie Mellon University, and we plan for a public release in the near future.

cs.RO↗

arXiv2020

Adaptive Matrix Completion for the Users and the Items in Tail

Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. 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. Also, we show that the number of ratings that an item or a user has positively correlates with the ability of low-rank matrix-completion-based approaches to predict the ratings for the item or the user accurately. Furthermore, we use these insights to develop four matrix completion-based approaches, i.e., Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization (TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional matrix-completion-based approaches for the users and the items with few ratings in the user-item rating matrix.

cs.IR↗

arXiv2019

Leveraging Multimodal Haptic Sensory Data for Robust Cutting

Cutting is a common form of manipulation when working with divisible objects such as food, rope, or clay. Cooking in particular relies heavily on cutting to divide food items into desired shapes. However, cutting food is a challenging task due to the wide range of material properties exhibited by food items. Due to this variability, the same cutting motions cannot be used for all food items. Sensations from contact events, e.g., when placing the knife on the food item, will also vary depending on the material properties, and the robot will need to adapt accordingly. In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events. The robot learns neural networks for performing each of these tasks and generalizing across different material properties. By adapting and monitoring the skill executions, the robot is able to reliably cut through more than 20 different types of food items and even detect whether certain food items are fresh or old.

cs.RO↗

arXiv2019

Intent term selection and refinement in e-commerce queries

In e-commerce, a user tends to search for the desired product by issuing a query to the search engine and examining the retrieved results. If the search engine was successful in correctly understanding the user's query, it will return results that correspond to the products whose attributes match the terms in the query that are representative of the query's product intent. However, the search engine may fail to retrieve results that satisfy the query's product intent and thus degrading user experience due to different issues in query processing: (i) when multiple terms are present in a query it may fail to determine the relevant terms that are representative of the query's product intent, and (ii) it may suffer from vocabulary gap between the terms in the query and the product's description, i.e., terms used in the query are semantically similar but different from the terms in the product description. Hence, identifying the terms that describe the query's product intent and predicting additional terms that describe the query's product intent better than the existing query terms to the search engine is an essential task in e-commerce search. In this paper, we leverage the historical query reformulation logs of a major e-commerce retailer to develop distant-supervised approaches to solve both these problems. Our approaches exploit the fact that the significance of a term is dependent upon the context (other terms in the neighborhood) in which it is used in order to learn the importance of the term towards the query's product intent. We show that identifying and emphasizing the terms that define the query's product intent leads to a 3% improvement in ranking. 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.

cs.IR↗

arXiv2019

An Attention Mechanism for Musical Instrument Recognition

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. Datasets for polyphonic instrument recognition can be categorized into roughly two categories. Some, such as MedleyDB, have strong per-frame instrument activity annotations but are usually small in size. Other, larger datasets such as OpenMIC only have weak labels, i.e., instrument presence or absence is annotated only for long snippets of a song. We explore an attention mechanism for handling weakly labeled data for multi-label instrument recognition. Attention has been found to perform well for other tasks with weakly labeled data. We compare the proposed attention model to multiple models which include a baseline binary relevance random forest, recurrent neural network, and fully connected neural networks. Our results show that incorporating attention leads to an overall improvement in classification accuracy metrics across all 20 instruments in the OpenMIC dataset. We find that attention enables models to focus on (or `attend to') specific time segments in the audio relevant to each instrument label leading to interpretable results.

cs.IR↗

arXiv2019

Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation

Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for TOP-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.

cs.IR↗

arXiv2019

Learning from Sets of Items in Recommender Systems

Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are two-fold. First, a rating provided on a set conveys some preference information about each of the set's items, which allows us to acquire a user's preferences for more items that the number of ratings that the user provided. Second, due to privacy concerns, users may not be willing to reveal their preferences on individual items explicitly but may be willing to provide a single rating to a set of items, since it provides some level of information hiding. This paper investigates two questions related to using set-level ratings in recommender systems. First, how users' item-level ratings relate to their set-level ratings. Second, how collaborative filtering-based models for item-level rating prediction can take advantage of such set-level ratings. We have collected set-level ratings from active users of Movielens on sets of movies that they have rated in the past. 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. We have developed collaborative filtering-based methods to explicitly model these user behaviors that can be used to recommend items to users. Experiments on real data and on synthetic data that resembles the under- or over-rating behavior in the real data, demonstrate that these models can recover the overall characteristics of the underlying data and predict the user's ratings on individual items.

cs.IR↗

arXiv2019

Learning Semantic Embedding Spaces for Slicing Vegetables

In this work, we present an interaction-based approach to learn semantically rich representations for the task of slicing vegetables. Unlike previous approaches, we focus on object-centric representations and use auxiliary tasks to learn rich representations using a two-step process. First, we use simple auxiliary tasks, such as predicting the thickness of a cut slice, to learn an embedding space which captures object properties that are important for the task of slicing vegetables. In the second step, we use these learned latent embeddings to learn a forward model. Learning a forward model affords us to plan online in the latent embedding space and forces our model to improve its representations while performing the slicing task. To show the efficacy of our approach we perform experiments on two different vegetables: cucumbers and tomatoes. Our experimental evaluation shows that our method is able to capture important semantic properties for the slicing task, such as the thickness of the vegetable being cut. We further show that by using our learned forward model, we can plan for the task of vegetable slicing.

cs.RO↗

arXiv2019

The life of a New York City noise sensor network

Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data has been collected that provides an indication of a sensors' health. This telemetry data was analyzed over an 18 month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.

cs.SD↗

arXiv2019

Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model. We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories. We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.

cs.LG↗

arXiv2018

Higher Order Nonlinear Dynamics in AlGaAs Doped Glass Photonic Crystal Fibers at Sub Pico-Joule Energy

In the present paper, a unique semiconductor doped glass photonic crystal fiber has been designed which is suitable for soliton propagation at sub pico-Joule energy. The fiber promises to yield low and uniform anomalous dispersion profile and very large optical nonlinearities 25211 W^(-1) km^(-1) at telecommunication wavelength, thus facilitating soliton formation at ultralow energy. The observed magnitude of nonlinearity is the highest reported ever till date in AlGaAs doped glass. The soliton dynamics has been investigated taking into account of higher-order dispersions and nonlinearities. Propagating solitons breadth and experience large frequency shift, which decreases with the increase in the initial pulse width and increases with the increase in pulse energy. Temporal width of solitons oscillate, the frequency and amplitude of oscillations increase with the increase in the value of pulse energy.

physics.optics↗

arXiv2017

Inverse Reinforcement Learning with Conditional Choice Probabilities

We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm using Conditional Choice Probabilities (CCP), which are maximum likelihood estimates of the policy estimated from expert demonstrations, to solve the IRL problem. Using the language of structural econometrics, we re-frame the optimal decision problem and introduce an alternative representation of value functions due to (Hotz and Miller 1993). In addition to presenting the theoretical connections that bridge the IRL literature between Economics and Robotics, the use of CCPs also has the practical benefit of reducing the computational cost of solving the IRL problem. Specifically, under the CCP representation, we show how one can avoid repeated calls to the dynamic programming subroutine typically used in IRL. We show via extensive experimentation on standard IRL benchmarks that CCP-IRL is able to outperform MaxEnt-IRL, with as much as a 5x speedup and without compromising on the quality of the recovered reward function.

cs.AI↗

arXiv2015

Three octave spanning supercontinuum by red-shifted dispersive wave in photonic crystal fibers

This paper presents a three layer index guided lead silicate (SF57) photonic crystal fiber which simultaneously promises to yield large effective optical nonlinear coefficient and low anomalous dispersion that makes it suitable for supercontinuum generation. At an operating wavelength 1550 nm, the typical optimized value of anomalous dispersion and effective nonlinear coefficient turns out to be ~4 ps/km/nm and ~1078 W^(-1) km^(-1), respectively. Through numerical simulation it is realized that the designed fiber promises to exhibit three octave spanning supercontinuum from 900 to 7200 nm by using 50 fs sech optical pulses of 5 kW peak power. Due to the cross-phase modulation and four-wave mixing processes, a long range of red-shifted dispersive wave generated, which assist to achieve such large broadening. In addition, we have investigated the compatibility of supercontinuum generation with input pulse peak power increment and briefly discussed the impact of nonlinear processes on supercontinuum generation.

physics.optics↗

arXiv2015

Supercontinuum Generation in Photonic Crystal Fibers Possessing High Birefringence and Large Optical Nonlinearity

This paper presents the design of an index guided highly birefringent photonic crystal fiber which promises to yield very large birefringence ~3.33 X 10^(-2) at 1550 nm and ~1.75 X 10^(-2) at 1064 nm as well as large effective nonlinearity ~80 W^(-1)km^(-1). Optical supercontinuum generation in the proposed fiber using a 1064 nm pump source with peak power of 1kW has been also presented. Finite difference time domain method (FDTD) has been employed to examine the optical properties such as fiber birefringence, mode field, V-parameter, walk-off and optical nonlinearity, while the Split-step Fourier method is used to solve the nonlinear Schrodinger equation felicitating the study of supercontinuum generation. Simulation results indicate that horizontal input pulse yields superior continuum in comparison to that of the vertically polarized input. However, the broadening of the continuum is about 1450 nm in case of horizontally polarized input light whereas it is approximately 2350 nm for vertically polarized.

physics.optics↗