We present an approach for performant point-goal navigation in unfamiliar partially-mapped environments. When deployed, our robot runs multiple strategies for deployment-time learning and visual domain adaptation in parallel and quickly selects the best-performing among them. Choosing between policies as they are learned or adapted between navigation trials requires continually updating estimates of their performance as they evolve. Leveraging recent work in model-based learning-informed planning under uncertainty, we determine lower bounds on the would-be performance of newly-updated policies on old trials without needing to re-deploy them. This information constrains and accelerates bandit-like policy selection, affording quick selection of the best-performing strategy shortly after it would start to yield good performance. We validate the effectiveness of our approach in simulated maze-like environments, showing improved navigation cost and cumulative regret versus existing baselines.
@inproceedings{paudel2024multistrategy,title={Multi-Strategy Deployment-Time Learning and Adaptation for Navigation under Uncertainty},author={Paudel, Abhishek and Xiao, Xuesu and Stein, Gregory J.},booktitle={Conference on Robot Learning (CoRL)},year={2024},url={https://openreview.net/forum?id=Isp19rFFV4},}
CoRL LEAP
Enhancing Object Search by Augmenting Planning with Predictions from Large Language Models
Hossain, Shahriar,
Paudel, Abhishek,
and Stein, Gregory J.
In CoRL Workshop on Learning Effective Abstractions for Planning (LEAP)
2024
We enhance object search in unknown environments by integrating a Large Language Model (LLM) with model-based planning to quickly and reliably locate an object of interest. The LLM is prompted to produce predictions about the likelihood of finding the object of interest used to define a model for planning that the robot then uses to determine its search policy, affording both good performance due to the integration of learning and reliability due to the reliance on classical planning to find the object. From our findings on 200 random maps on the ProcTHOR dataset, our proposed LLM-informed planner, utilizing GPT-4o predictions, achieves a cost reduction of 27.1% and 30.3% compared to the standard baseline and Myopic LLM-informed baseline, respectively.
@inproceedings{hossain2024enhancing,title={Enhancing Object Search by Augmenting Planning with Predictions from Large Language Models},author={Hossain, Shahriar and Paudel, Abhishek and Stein, Gregory J.},booktitle={CoRL Workshop on Learning Effective Abstractions for Planning (LEAP)},year={2024},url={https://openreview.net/forum?id=qQPp4MZbpb},}
2023
IEEE/RSJ IROS
Data-Efficient Policy Selection for Navigation in Partial Maps via Subgoal-Based Abstraction
Paudel, Abhishek,
and Stein, Gregory J.
In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2023
We present a novel approach for fast and reliable policy selection for navigation in partial maps. Leveraging the recent learning-augmented model-based Learning over Subgoals Planning (LSP) abstraction to plan, our robot reuses data collected during navigation to evaluate how well other alternative policies could have performed via a procedure we call offline alt-policy replay. Costs from offline alt-policy replay constrain policy selection among the LSP-based policies during deployment, allowing for improvements in convergence speed, cumulative regret and average navigation cost. With only limited prior knowledge about the nature of unseen environments, we achieve at least 67% and as much as 96% improvements on cumulative regret over the baseline bandit approach in our experiments in simulated maze and office-like environments.
@inproceedings{paudel2023dataefficient,title={Data-Efficient Policy Selection for Navigation in Partial Maps via Subgoal-Based Abstraction},author={Paudel, Abhishek and Stein, Gregory J.},booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},year={2023},doi={10.1109/IROS55552.2023.10342047},url={https://ieeexplore.ieee.org/document/10342047},organization={IEEE},}
2022
arXiv
Motion Primitives based Path Planning with Rapidly-exploring Random Tree
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.
@misc{paudel2022motion,title={Motion Primitives based Path Planning with Rapidly-exploring Random Tree},author={Paudel, Abhishek},year={2022},doi={10.48550/ARXIV.2210.15784},publisher={arXiv},}
arXiv
Learning for Robot Decision Making under Distribution Shift: A Survey
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.
@misc{paudel2022learning,title={Learning for Robot Decision Making under Distribution Shift: A Survey},author={Paudel, Abhishek},year={2022},doi={10.48550/ARXIV.2203.07558},publisher={arXiv},}
2021
arXiv
Room Classification on Floor Plan Graphs using Graph Neural Networks
Paudel, Abhishek,
Dhakal, Roshan,
and Bhattarai, Sakshat
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.
@misc{paudel2021room,title={Room Classification on Floor Plan Graphs using Graph Neural Networks},author={Paudel, Abhishek and Dhakal, Roshan and Bhattarai, Sakshat},year={2021},doi={10.48550/ARXIV.2108.05947},publisher={arXiv},}
arXiv
Sophisticated Students in Boston Mechanism and Gale-Shapley Algorithm for School Choice Problem
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.
@misc{paudel2021sophisticated,title={Sophisticated Students in Boston Mechanism and Gale-Shapley Algorithm for School Choice Problem},author={Paudel, Abhishek},year={2021},doi={10.48550/ARXIV.2108.05951},publisher={arXiv},}
2018
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
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.
@inproceedings{paudel2018using,author={Paudel, Abhishek and Bajracharya, Brihat Ratna and Ghimire, Miran and Bhattarai, Nabin and Baral, Daya Sagar},booktitle={2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)},title={Using Personality Traits Information from Social Media for Music Recommendation},year={2018},pages={116-121},doi={10.1109/CCCS.2018.8586831},url={https://ieeexplore.ieee.org/document/8586831},}