I work on embodied intelligence for assistive navigation for blind and low-vision travelers: reinforcement learning and vision-language-action policies that operate under partial observability and within the memory, latency, and power constraints of onboard compute. Much of this is part of our lab’s guide dog robot project.
Before UMass, I was a research assistant at CCDS, Independent University Bangladesh, where I worked on VLM-based agent-centric representations for RL with Dr. Riashat Islam (Mila, McGill University & Microsoft Research), and on multimodal reasoning and LLM-based multi-agent collaboration with Dr. Amin Ahsan Ali and Dr. AKM Mahbubur Rahman of CCDS, IUB and Muntasir Wahed of UIUC. I did my B.Sc. in Computer Science and Engineering at the University of Dhaka, graduating with the Dean’s Honor Award; my thesis with Dr. Md Mosaddek Khan was on decentralized multi-agent reinforcement learning.
NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation
Hochul Hwang*, Jahir Sadik Monon*, Soowan Yang*, Sahil U. Patel, Anh N. H. Nguyen, Khoa Nguyen, Keshav Garg, Daniel Gage, Dawit Hordofaa, Anjna Anjna, Eshed Ohn-Bar, and Donghyun Kim
In NeurIPS Datasets and Benchmarks Track (under review), 2026
@inproceedings{hwang2026navable,title={NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation},author={Hwang, Hochul and Monon, Jahir Sadik and Yang, Soowan and Patel, Sahil U. and Nguyen, Anh N. H. and Nguyen, Khoa and Garg, Keshav and Gage, Daniel and Hordofaa, Dawit and Anjna, Anjna and Ohn-Bar, Eshed and Kim, Donghyun},booktitle={NeurIPS Datasets and Benchmarks Track (under review)},year={2026},}
While commendable progress has been made in user-centric research on mobile assistive systems for blind and low-vision (BLV) individuals, references that directly inform robot navigation design remain rare. GuideNav is a vision-only robotic navigation assistant for blind travelers, developed through user-informed design with the BLV community.
@inproceedings{hwang2026guidenav,title={GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant for Blind Travelers},author={Hwang, Hochul and Yang, Soowan and Monon, Jahir Sadik and Giudice, Nicholas A. and Lee, Sunghoon Ivan and Biswas, Joydeep and Kim, Donghyun},booktitle={Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI)},year={2026},}
Multi-agent Reinforcement Learning (MARL) is emerging as a key framework for various sequential decision-making and control tasks. Unlike their single-agent counterparts, multi-agent systems necessitate successful cooperation among the agents. The real-world deployment of these systems requires decentralized training and execution (DTE), diverse agents, and learning from infrequent environmental rewards. These challenges become more pronounced under partial observability and the lack of prior knowledge about agent heterogeneity. While notable studies use intrinsic motivation (IM) to address reward sparsity or cooperation in decentralized execution settings, those dealing with heterogeneity typically assume centralized training for decentralized execution (CTDE). To overcome these limitations, we propose the CoHet algorithm, which utilizes a novel Graph Neural Network (GNN) based intrinsic motivation to facilitate the learning of heterogeneous agent policies in fully decentralized settings, under the challenges of partial observability and reward sparsity. Evaluation of CoHet in the Multi-agent Particle Environment (MPE) and Vectorized Multi-Agent Simulator (VMAS) benchmarks demonstrates superior performance compared to the state-of-the-art in a range of cooperative multi-agent scenarios.
@inproceedings{monon2025cohet,title={Learning Heterogeneous Agent Collaboration in Decentralized Multi-Agent Systems via Intrinsic Motivation},author={Monon, Jahir Sadik and Barua, Deeparghya Dutta and Khan, Md. Mosaddek},booktitle={Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},year={2025},}
