arXiv subjects

Arun Kumar Singh

Publications and source records attributed to Arun Kumar Singh.

52 records · Page 3Linked to original sources

arXiv2021

Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition

The recent developments in technology have re-warded us with amazing audio synthesis models like TACOTRON and WAVENETS. On the other side, it poses greater threats such as speech clones and deep fakes, that may go undetected. To tackle these alarming situations, there is an urgent need to propose models that can help discriminate a synthesized speech from an actual human speech and also identify the source of such a synthesis. Here, we propose a model based on Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (BiRNN) that helps to achieve both the aforementioned objectives. The temporal dependencies present in AI synthesized speech are exploited using Bidirectional RNN and CNN. The model outperforms the state-of-the-art approaches by classifying the AI synthesized audio from real human speech with an error rate of 1.9% and detecting the underlying architecture with an accuracy of 97%.

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arXiv2021

Detection of AI-Synthesized Speech Using Cepstral & Bispectral Statistics

Digital technology has made possible unimaginable applications come true. It seems exciting to have a handful of tools for easy editing and manipulation, but it raises alarming concerns that can propagate as speech clones, duplicates, or maybe deep fakes. Validating the authenticity of a speech is one of the primary problems of digital audio forensics. We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. We integrate both these analyses and propose a machine learning model to detect AI synthesized speech.

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arXiv2020

GPU Accelerated Convex Approximations for Fast Multi-Agent Trajectory Optimization

In this paper, we present a computationally efficient trajectory optimizer that can exploit GPUs to jointly compute trajectories of tens of agents in under a second. At the heart of our optimizer is a novel reformulation of the non-convex collision avoidance constraints that reduces the core computation in each iteration to that of solving a large scale, convex, unconstrained Quadratic Program (QP). We also show that the matrix factorization/inverse computation associated with the QP needs to be done only once and can be done offline for a given number of agents. This further simplifies the solution process, effectively reducing it to a problem of evaluating a few matrix-vector products. Moreover, for a large number of agents, this computation can be trivially accelerated on GPUs using existing off-the-shelf libraries. We validate our optimizer's performance on challenging benchmarks and show substantial improvement over state of the art in computation time and trajectory quality.

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arXiv2020

Fast Adaptation of Manipulator Trajectories to Task Perturbation By Differentiating through the Optimal Solution

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. But most importantly, our algorithm achieves a worst-case speed-up of 160x over the latter approach.

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arXiv2020

A Benchmark Corpus and Neural Approach for Sanskrit Derivative Nouns Analysis

This paper presents first benchmark corpus of Sanskrit Pratyaya (suffix) and inflectional words (padas) formed due to suffixes along with neural network based approaches to process the formation and splitting of inflectional words. Inflectional words spans the primary and secondary derivative nouns as the scope of current work. Pratyayas are an important dimension of morphological analysis of Sanskrit texts. There have been Sanskrit Computational Linguistics tools for processing and analyzing Sanskrit texts. Unfortunately there has not been any work to standardize & validate these tools specifically for derivative nouns analysis. In this work, we prepared a Sanskrit suffix benchmark corpus called Pratyaya-Kosh to evaluate the performance of tools. We also present our own neural approach for derivative nouns analysis while evaluating the same on most prominent Sanskrit Morphological Analysis tools. This benchmark will be freely dedicated and available to researchers worldwide and we hope it will motivate all to improve morphological analysis in Sanskrit Language.

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arXiv2020

Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit Language

This paper describes neural network based approaches to the process of the formation and splitting of word-compounding, respectively known as the Sandhi and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to morphological analysis of Sanskrit texts. Sandhi leads to word transformations at word boundaries. The rules of Sandhi formation are well defined but complex, sometimes optional and in some cases, require knowledge about the nature of the words being compounded. Sandhi split or Vichchhed is an even more difficult task given its non uniqueness and context dependence. In this work, we propose the route of formulating the problem as a sequence to sequence prediction task, using modern deep learning techniques. Being the first fully data driven technique, we demonstrate that our model has an accuracy better than the existing methods on multiple standard datasets, despite not using any additional lexical or morphological resources. The code is being made available at https://github.com/IITD-DataScience/Sandhi_Prakarana

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arXiv2020

Reactive Navigation under Non-Parametric Uncertainty through Hilbert Space Embedding of Probabilistic Velocity Obstacles

The probabilistic velocity obstacle (PVO) extends the concept of velocity obstacle (VO) to work in uncertain dynamic environments. In this paper, we show how a robust model predictive control (MPC) with PVO constraints under non-parametric uncertainty can be made computationally tractable. At the core of our formulation is a novel yet simple interpretation of our robust MPC as a problem of matching the distribution of PVO with a certain desired distribution. To this end, we propose two methods. Our first baseline method is based on approximating the distribution of PVO with a Gaussian Mixture Model (GMM) and subsequently performing distribution matching using Kullback Leibler (KL) divergence metric. Our second formulation is based on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to interpret our robust MPC as a problem of minimizing the distance between the desired distribution and the distribution of the PVO in the RKHS. Both the RKHS and GMM based formulation can work with any uncertainty distribution and thus allowing us to relax the prevalent Gaussian assumption in the existing works. We validate our formulation by taking an example of 2D navigation of quadrotors with a realistic noise model for perception and ego-motion uncertainty. In particular, we present a systematic comparison between the GMM and the RKHS approach and show that while both approaches can produce safe trajectories, the former is highly conservative and leads to poor tracking and control costs. Furthermore, RKHS based approach gives better computational times that are up to one order of magnitude lesser than the computation time of the GMM based approach.

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arXiv2019

Inducing Multi-Convexity in Path Constrained Trajectory Optimization for Mobile Manipulators

In this paper, we propose a novel trajectory optimization algorithm for mobile manipulators under end-effector path, collision avoidance and various kinematic constraints. Our key contribution lies in showing how this highly non-linear and non-convex problem can be solved as a sequence of convex unconstrained quadratic programs (QPs). This is achieved by reformulating the non-linear constraints that arise out of manipulator kinematics and its coupling with the mobile base in a multi-affine form. We then use techniques from Alternating Direction Method of Multipliers (ADMM) to formulate and solve the trajectory optimization problem. The proposed ADMM has two similar non-convex steps. Importantly, a convex surrogate can be derived for each of them. We show how large parts of our optimization can be solved in parallel providing the possibility of exploiting multi-core CPUs/GPUs. We validate our trajectory optimization on different benchmark examples. Specifically, we highlight how it solves the cyclicity bottleneck and provides a holistic approach where diverse set of trajectories can be obtained by trading-off different aspects of manipulator and mobile base motion.

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arXiv2018

Solving Chance Constrained Optimization under Non-Parametric Uncertainty Through Hilbert Space Embedding

In this paper, we present an efficient algorithm for solving a class of chance constrained optimization under non-parametric uncertainty. Our algorithm is built on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to formulate chance constrained optimization as one of minimizing the distance between a desired distribution and the distribution of the constraint functions in the RKHS. We provide a systematic way of constructing the desired distribution based on a notion of scenario approximation. Furthermore, we use the kernel trick to show that the computational complexity of our reformulated optimization problem is comparable to solving a deterministic variant of the chance-constrained optimization. We validate our formulation on two important robotic/control applications: (i) reactive collision avoidance of mobile robots in uncertain dynamic environments and (ii) inverse dynamics based path tracking of manipulators under perception uncertainty. In both these applications, the underlying chance constraints are defined over highly non-linear and non-convex functions of the uncertain parameters and possibly also decision variables. We also benchmark our formulation with the existing approaches in terms of sample complexity and the achieved optimal cost highlighting significant improvements in both these metrics.

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arXiv2018

Model Predictive Control for Autonomous Driving considering Actuator Dynamics

In this paper, we propose a new model predictive control (MPC) formulation for autonomous driving. The novelty of our MPC stems from the following results. Firstly, we adopt an alternating minimization approach wherein linear velocities and angular accelerations are alternately optimized. We show that in contrast to the joint optimization, the alternating minimization exploits the structure of the problem better, which in turn translates to reduction in computation time. Secondly, our MPC explicitly incorporates the time dependent non-linear actuator dynamics that captures the transient response of the vehicle for a given commanded velocity. This added complexity improves the predictive component of MPC resulting in improved margin of inter-vehicle distance during maneuvers like overtaking, lane-change, etc. Although, past works have also incorporated actuator dynamics within MPC, there has been very few attempts towards coupling actuator dynamics to collision avoidance constraints through the non-holonomic motion model of the vehicle and analyzing the resulting behavior. We use a high fidelity simulator to benchmark our actuator dynamics augmented MPC with other related approaches in terms of metrics like inter-vehicle distance, trajectory smoothness, and velocity overshoot.

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arXiv2018

Stochastic Optimal Control for Modeling Reaching Movements in the Presence of Obstacles: Theory and Simulation

In many human-in-the-loop robotic applications such as robot-assisted surgery and remote teleoperation, predicting the intended motion of the human operator may be useful for successful implementation of shared control, guidance virtual fixtures, and predictive control. Developing computational models of human movements is a critical foundation for such motion prediction frameworks. With this motivation, we present a computational framework for modeling reaching movements in the presence of obstacles. We propose a stochastic optimal control framework that consists of probabilistic collision avoidance constraints and a cost function that trades-off between effort and end-state variance in the presence of a signal-dependent noise. First, we present a series of reformulations to convert the original non-linear and non-convex optimal control into a parametric quadratic programming problem. We show that the parameters can be tuned to model various collision avoidance strategies, thereby capturing the quintessential variability associated with human motion. Then, we present a simulation study that demonstrates the complex interaction between avoidance strategies, control cost, and the probability of collision avoidance. The proposed framework can benefit a variety of applications that require teleoperation in cluttered spaces, including robot-assisted surgery. In addition, it can also be viewed as a new optimizer which produces smooth and probabilistically-safe trajectories under signal dependent noise.

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arXiv2018

Combining Method of Alternating Projections and Augmented Lagrangian for Task Constrained Trajectory Optimization

Motion planning for manipulators under task space constraints is difficult as it constrains the joint configurations to always lie on an implicitly defined manifold. It is possible to view task constrained motion planning as an optimization problem with non-linear equality constraints which can be solved by general non-linear optimization techniques. In this paper, we present a novel custom optimizer which exploits the underlying structure present in many task constraints. At the core of our approach are some simple reformulations, which when coupled with the \emph{method of alternating projection}, leads to an efficient convex optimization based routine for computing a feasible solution to the task constraints. We subsequently build on this result and use the concept of Augmented Lagrangian to guide the feasible solutions towards those which also minimize the user defined cost function. We show that the proposed optimizer is fully distributive and thus, can be easily parallelized. We validate our formulation on some common robotic benchmark problems. In particular, we show that the proposed optimizer achieves cyclic motion in the joint space corresponding to a similar nature trajectory in the task space. Furthermore, as a baseline, we compare the proposed optimizer with an off-the-shelf non-linear solver provide in open source package SciPy. We show that for similar task constraint residuals and smoothness cost, it can be upto more than three times faster than the SciPy alternative.

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arXiv2018

Trajectory Optimization for Curvature Bounded Non-Holonomic Vehicles: Application to Autonomous Driving

In this paper, we propose a trajectory optimization for computing smooth collision free trajectories for nonholonomic curvature bounded vehicles among static and dynamic obstacles. One of the key novelties of our formulation is a hierarchal optimization routine which alternately operates in the space of angular accelerations and linear velocities. That is, the optimization has a two layer structure wherein angular accelerations are optimized keeping the linear velocities fixed and vice versa. If the vehicle/obstacles are modeled as circles than the velocity optimization layer can be shown to have the computationally efficient difference of convex structure commonly observed for linear systems. This leads to a less conservative approximation as compared to that obtained by approximating each polygon individually through its circumscribing circle. On the other hand, it leads to optimization problem with less number of constraints as compared to that obtained when approximating polygons as multiple overlapping circles. We use the proposed trajectory optimization as the basis for constructing a Model Predictive Control framework for navigating an autonomous car in complex scenarios like overtaking, lane changing and merging. Moreover, we also highlight the advantages provided by the alternating optimization routine. Specifically, we show it produces trajectories which have comparable arc lengths and smoothness as compared to those produced with joint simultaneous optimization in the space of angular accelerations and linear velocities. However, importantly, the alternating optimization provides some gain in computational time.

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arXiv2018

Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone

In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem. We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.

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arXiv2016

Chance constraint based multi agent navigation under uncertainty

We present Probabilistic Reciprocal Velocity Obstacle or PRVO as a general algorithm for navigating multiple robots under perception and motion uncertainty. PRVO is defined as the space of velocities that ensures dynamic collision avoidance between a pair of robots with a specified probability. Our approach is based on defining chance constraints over the inequalities defined by the deterministic Reciprocal Velocity Obstacle (RVO). The computational complexity of the proposed probabilistic RVO is comparable to the deterministic counterpart. This is achieved by a series of reformulations where we first substitute the computationally intractable chance constraints with a family of surrogate constraints and then adopt a time scaling based solution methodology to efficiently characterize their solution space. Further, we also show that the solution space of each member of the family of surrogate constraints can be mapped in closed form to the probability with which the original chance constraints are satisfied and thus consequently to probability of collision avoidance. We validate our formulations through numerical simulations where we highlight the importance of incorporating the effect of motion uncertainty and the advantages of PRVO over existing formulations which handles the effect of uncertainty by using conservative bounding volumes.

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arXiv2014

Model Predictive Control for Micro Aerial Vehicle Systems (MAV) Systems

This paper presents a method for path-following for quadcopter trajectories in real time. Non-Linear Guidance Logic is used to find the intercepts of the subsequent destination. Trajectory tracking is implemented by formulating the trajectory of the quadcopter using its jerk, in discrete time, and then solving a convex optimization problem on each decoupled axis. Based on the maximum possible thrust and angular rates of the quadcopter, feasibility constraints for the quadcopter have been derived. In this report we describe the design and implementation of explicit MPC controllers where the controllers were executed on a computer using sparse solvers to control the vehicle in hovering flight.

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