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Arjun Sharma

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3 papers

2 author rows

ICRA Conference 2024 Conference Paper

TerrainSense: Vision-Driven Mapless Navigation for Unstructured Off-Road Environments

Bilal Hassan
Arjun Sharma
Nadya Abdel Madjid
Majid Khonji
Jorge Dias 0001

Navigating autonomous vehicles efficiently across unstructured and off-road terrains remains a formidable challenge, often requiring intricate mapping or multi-step pipelines. However, these conventional approaches struggle to adapt to dynamic environments. This paper presents TerrainSense, an end-to-end framework that overcomes these limitations. By utilizing a transformers, TerrainSense detects lane semantics and topology from camera images, enabling mapless path planning without the reliance on highly detailed maps. The efficacy of TerrainSense was rigorously assessed on six diverse datasets, evaluating its efficacy in detection, segmentation, and path prediction using various metrics. Notably, it outperforms the other state-of-the-art methods by 9. 32% in precisely predicting the path with 18. 28% faster inference time.

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NeurIPS Conference 2021 Conference Paper

Chest ImaGenome Dataset for Clinical Reasoning

Joy T Wu
Nkechinyere Agu
Ismini Lourentzou
Arjun Sharma
Joseph Alexander Paguio
Jasper Seth Yao
Edward C Dee
William Mitchell

Despite the progress in automatic detection of radiologic findings from Chest X-Ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings. With the exception of a few expert-labeled small-scale datasets for specific findings, such as pneumonia and pneumothorax, most of the CXR deep learning models to date are trained on global "weak" labels extracted from text reports, or trained via a joint image and unstructured text learning strategy. Inspired by the Visual Genome effort in the computer vision community, we constructed the first Chest ImaGenome dataset with a scene graph data structure to describe 242, 072 images. Local annotations are automatically produced using a joint rule-based natural language processing (NLP) and atlas-based bounding box detection pipeline. Through a radiologist constructed CXR ontology, the annotations for each CXR are connected as an anatomy-centered scene graph, useful for image-level reasoning and multimodal fusion applications. Overall, we provide: i) 1, 256 combinations of relation annotations between 29 CXR anatomical locations (objects with bounding box coordinates) and their attributes, structured as a scene graph per image, ii) over 670, 000 localized comparison relations (for improved, worsened, or no change) between the anatomical locations across sequential exams, as well as ii) a manually annotated gold standard scene graph dataset from 500 unique patients.

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AAAI Conference 2018 Conference Paper

Phase-Parametric Policies for Reinforcement Learning in Cyclic Environments

Arjun Sharma
Kris Kitani

In many reinforcement learning problems, parameters of the model may vary with its phase while the agent attempts to learn through its interaction with the environment. For example, an autonomous car’s reward on selecting a path may depend on trafﬁc conditions at the time of the day or the transition dynamics of a drone may depend on the current wind direction. Many such processes exhibit a cyclic phase-structure and could be represented with a control policy parameterized over a circular or cyclic phase space. Attempting to model such phase variations with a standard data-driven approach (e. g. deep networks) without explicitly modeling the phase of the model can be challenging. Ambiguities may arise as the optimal action for a given state can vary depending on the phase. To better model cyclic environments, we propose phase-parameterized policies and value function approximators that explicitly enforce a cyclic structure to the policy or value space. We apply our phase-parameterized reinforcement learning approach to both feed-forward and recurrent deep networks in the context of trajectory optimization and locomotion problems. Our experiments show that our proposed approach has superior modeling performance than traditional function approximators in cyclic environments.

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