arXiv subjects

Meet Gandhi

Publications and source records attributed to Meet Gandhi.

6 recordsLinked to original sources

arXiv2026

Distributed Quantum Gaussian Processes for Multi-Agent Systems

Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale real-world domains due to the limited expressivity of classical kernels. Quantum computing offers the potential to overcome this limitation by embedding data into exponentially large Hilbert spaces, capturing complex correlations that remain inaccessible to classical computing approaches. In this paper, we propose a Distributed Quantum Gaussian Process (DQGP) method in a multi-agent setting to enhance modeling capabilities and scalability. To address the challenging non-Euclidean optimization problem, we develop a Distributed consensus Riemannian Alternating Direction Method of Multipliers (DR-ADMM) algorithm that aggregates local agent models into a global model. We evaluate the efficacy of our method through numerical experiments conducted on a quantum simulator in classical hardware. We use real-world, non-stationary elevation datasets of NASA's Shuttle Radar Topography Mission and synthetic datasets generated by Quantum Gaussian Processes. Beyond modeling advantages, our framework highlights potential computational speedups that quantum hardware may provide, particularly in Gaussian processes and distributed optimization.

cs.MA↗

arXiv2024

ATLS: Automated Trailer Loading for Surface Vessels

Automated docking technologies of marine boats have been enlightened by an increasing number of literature. This paper contributes to the literature by proposing a mathematical framework that automates "trailer loading" in the presence of wind disturbances, which is unexplored despite its importance to boat owners. The comprehensive pipeline of localization, system identification, and trajectory optimization is structured, followed by several techniques to improve performance reliability. The performance of the proposed method was demonstrated with a commercial pontoon boat in Michigan, in 2023, securing a success rate of 80\% in the presence of perception errors and wind disturbance. This result indicates the strong potential of the proposed pipeline, effectively accommodating the wind effect.

eess.SY↗

arXiv2022

Design of an Autonomous Agriculture Robot for Real Time Weed Detection using CNN

Agriculture has always remained an integral part of the world. As the human population keeps on rising, the demand for food also increases, and so is the dependency on the agriculture industry. But in today's scenario, because of low yield, less rainfall, etc., a dearth of manpower is created in this agricultural sector, and people are moving to live in the cities, and villages are becoming more and more urbanized. On the other hand, the field of robotics has seen tremendous development in the past few years. The concepts like Deep Learning (DL), Artificial Intelligence (AI), and Machine Learning (ML) are being incorporated with robotics to create autonomous systems for various sectors like automotive, agriculture, assembly line management, etc. Deploying such autonomous systems in the agricultural sector help in many aspects like reducing manpower, better yield, and nutritional quality of crops. So, in this paper, the system design of an autonomous agricultural robot which primarily focuses on weed detection is described. A modified deep-learning model for the purpose of weed detection is also proposed. The primary objective of this robot is the detection of weed on a real-time basis without any human involvement, but it can also be extended to design robots in various other applications involved in farming like weed removal, plowing, harvesting, etc., in turn making the farming industry more efficient. Source code and other details can be found at https://github.com/Dhruv2012/Autonomous-Farm-Robot

cs.RO↗

arXiv2022

Audiomer: A Convolutional Transformer For Keyword Spotting

Transformers have seen an unprecedented rise in Natural Language Processing and Computer Vision tasks. However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or incur a performance penalty when trained on Fourier-based features. In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in keyword spotting with raw audio waveforms, outperforming all previous methods while being computationally cheaper and parameter-efficient. Additionally, our model has practical advantages for speech processing, such as inference on arbitrarily long audio clips owing to the absence of positional encoding. The code is available at https://github.com/The-Learning-Machines/Audiomer-PyTorch.

cs.LG↗

arXiv2021

Schedule Based Temporal Difference Algorithms

Learning the value function of a given policy from data samples is an important problem in Reinforcement Learning. TD($λ$) is a popular class of algorithms to solve this problem. However, the weights assigned to different $n$-step returns in TD($λ$), controlled by the parameter $λ$, decrease exponentially with increasing $n$. In this paper, we present a $λ$-schedule procedure that generalizes the TD($λ$) algorithm to the case when the parameter $λ$ could vary with time-step. This allows flexibility in weight assignment, i.e., the user can specify the weights assigned to different $n$-step returns by choosing a sequence $\{λ_t\}_{t \geq 1}$. Based on this procedure, we propose an on-policy algorithm - TD($λ$)-schedule, and two off-policy algorithms - GTD($λ$)-schedule and TDC($λ$)-schedule, respectively. We provide proofs of almost sure convergence for all three algorithms under a general Markov noise framework.

cs.LG↗

arXiv2020

A reinforcement learning approach to hybrid control design

In this paper we design hybrid control policies for hybrid systems whose mathematical models are unknown. Our contributions are threefold. First, we propose a framework for modelling the hybrid control design problem as a single Markov Decision Process (MDP). This result facilitates the application of off-the-shelf algorithms from Reinforcement Learning (RL) literature towards designing optimal control policies. Second, we model a set of benchmark examples of hybrid control design problem in the proposed MDP framework. Third, we adapt the recently proposed Proximal Policy Optimisation (PPO) algorithm for the hybrid action space and apply it to the above set of problems. It is observed that in each case the algorithm converges and finds the optimal policy.

eess.SY↗