publications

2022

1. arXiv

   Motion Primitives based Path Planning with Rapidly-exploring Random Tree

   Paudel, Abhishek

   2022

   Abs arXiv PDF Code

   We present an approach that generates kinodynamically feasible paths for robots using Rapidly-exploring Random Tree (RRT). We leverage motion primitives as a way to capture the dynamics of the robot and use these motion primitives to build branches of the tree with RRT. Since every branch is built using the robot’s motion primitives that doesn’t lead to collision with obstacles, the resulting path is guaranteed to satisfy the robot’s kinodynamic constraints and thus be feasible for navigation without any post-processing on the generated trajectory. We demonstrate the effectiveness of our approach in simulated 2D environments using simple robot models with a variety of motion primitives.

2. arXiv

   Learning for Robot Decision Making under Distribution Shift: A Survey

   Paudel, Abhishek

   2022

   Abs arXiv PDF

   With the recent advances in the field of deep learning, learning-based methods are widely being implemented in various robotic systems that help robots understand their environment and make informed decisions to achieve a wide variety of tasks or goals. However, learning-based methods have repeatedly been shown to have poor generalization when they are presented with inputs that are different from those during training leading to the problem of distribution shift. Any robotic system that employs learning-based methods is prone to distribution shift which might lead the agents to make decisions that lead to degraded performance or even catastrophic failure. In this paper, we discuss various techniques that have been proposed in the literature to aid or improve decision making under distribution shift for robotic systems. We present a taxonomy of existing literature and present a survey of existing approaches in the area based on this taxonomy. Finally, we also identify a few open problems in the area that could serve as future directions for research.

2021

1. arXiv

   Room Classification on Floor Plan Graphs using Graph Neural Networks

   Paudel, Abhishek, Dhakal, Roshan, and Bhattarai, Sakshat

   2021

   Abs arXiv PDF Code

   We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories. Rooms in the floor plans are represented as nodes in the graph with edges representing their adjacency in the map. We experiment with House-GAN dataset that consists of floor plan maps in vector format and train multilayer perceptron and graph neural networks. Our results show that graph neural networks, specifically GraphSAGE and Topology Adaptive GCN were able to achieve accuracy of 80% and 81% respectively outperforming baseline multilayer perceptron by more than 15% margin.

2. arXiv

   Sophisticated Students in Boston Mechanism and Gale-Shapley Algorithm for School Choice Problem

   Paudel, Abhishek

   2021

   Abs arXiv PDF Code

   We present our experimental results of simulating the school choice problem which deals with the assignment of students to schools based on each group’s complete preference list for the other group using two algorithms: Boston mechanism and student-proposing Gale-Shapley algorithm. We compare the effects of sophisticated students altering their preference lists with regards to these two algorithms. Our simulation results show that sophisticated students can benefit more in Boston mechanism compared to Gale-Shapley algorithm based on multiple evaluation metrics.

2018

1. IEEE ICCCS

   Using Personality Traits Information from Social Media for Music Recommendation

   Paudel, Abhishek, Bajracharya, Brihat Ratna, Ghimire, Miran, Bhattarai, Nabin, and Baral, Daya Sagar

   In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) 2018

   Abs HTML Code

   Music is an integral part of our life. People listen to music everyday as per their taste and mood. With the advancement and increase in volume of digital content, the choice for people to listen to diverse type of music has also increased significantly. Thus, the necessity of delivering the most suited music to the listeners has been an interesting field of research in computer science. One of the important measures to deliver the best music to listeners could be their personality traits. In order to determine the personality traits of a person, social media like Facebook can be a useful platform where people express their views on different matters, share their opinions and thoughts. This paper first describes the use of Naive Bayes classifier to determine the standard Big Five Personality Traits of a person based on their status updates on Facebook profile using basic natural language processing techniques, and then proceeds to present the use of thus obtained information about personality traits to enhance the widely implemented user-to-user collaborative filtering techniques for music recommendation.