Offline goal-conditioned RL
Learning to reach any goal from a fixed dataset. I study value learning that stays correct over long horizons and under stochastic dynamics, and which representations actually help a goal-reaching agent act better.
Research Assistant · AVIS Lab, University of Dhaka
Abdul Monaf Chowdhury
I work on embodied agents that learn long-horizon skills from offline data and from language feedback.
Applying for PhD positions · Fall 2027I’m a Research Assistant at the AVIS Lab, University of Dhaka, working on offline goal-conditioned reinforcement learning and long-horizon robot manipulation. Most recently, I grounded divide-and-conquer value learning with temporal differences so it holds up under stochastic dynamics (GTRL), and turned a vision–language model’s reflections on failed episodes into reward for robot manipulation (LAGEA, ICML 2026).
Before this, I was a Research Assistant at the MAIM Lab, building a wearable fetal-movement monitor for stillbirth prevention, a Wellcome Leap In Utero project with Dr. Abhishek Kumar Ghosh and Dr. Niamh Nowlan. I hold a BSc in Robotics and Mechatronics Engineering from the University of Dhaka (2024), where Dr. Md Mehedi Hasan supervised my thesis on UAV-based human action recognition.
Research
I want agents that can carry out long, multi-step tasks in the real world. That takes value estimates that stay accurate over long horizons, feedback that tells an agent where an attempt went wrong and not just that it failed, and representations that bring together what an agent sees, reads and measures. My work so far follows three threads.
Language-guided embodied agents
Vision–language models as critics for robot learning: structured reflections on failed episodes, grounded in time and turned into dense reward shaping for manipulation.
Multimodal learning
Fusing complementary views of one signal, such as time, frequency and language, or the visible and hidden parts of a scene, into representations that hold up with little data.
Fall 2027
Looking for a PhD position
I’m applying to PhD programs starting in Fall 2027. I want to keep building embodied agents that learn long-horizon skills, where reinforcement learning, vision–language models and robot manipulation meet. My first-author work has appeared at ICML 2026 and AAAI 2026.
If you work on robot learning, embodied AI or reinforcement learning and think we’d be a good fit, I’d love to hear from you.
News
All news →- Submitted two papers to
ICLR '27:GTRL: Grounding Divide-and-Conquer Value Learning with Temporal DifferencesandDo Better Goal Representations Improve Goal-Conditioned Reinforcement Learning? - My paper titled
LAGEA: Language Guided Embodied Agents for Robotic Manipulationgot accepted for a Poster atICML '26 - Submitted my work on Open World Amodal Counting to
ECCV '26, and the paper is published atArXiv - My paper titled
T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusiongot accepted for a Poster atAAAI '26 - My journal paper titled
U-ActionNet: Dual-Pathway Fourier Networks with Region-of-Interest Module for Efficient Action Recognition in UAV Surveillance, based on my fourth year thesis work, has been published in IEEE Access - Our work on stillbirth prevention got another year of funding extension worth $1M by Wellcome Leap - In Utero
- Graduated from
University of Dhaka, Bangladeshwith a BSc. in Robotics & Mechatronics Engineering. [Report] - My paper titled
FFT-UAVNet: FFT Based Human Action Recognition for Drone Surveillance Systemgot accepted to the 5th IEEE International Conference on Sustainable Technologies for Industry 5.0 (STI) conference
Selected publications
All publications →
arXiv 2026
GTRL: Grounding Divide-and-Conquer Value Learning with Temporal Differences
Grounding divide-and-conquer value learning with temporal differences, so offline goal-conditioned RL stays correct when the dynamics are stochastic.
Preprint 2026
Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?
Handed a perfect goal representation, a goal-conditioned agent barely acts better. The headroom is in how it sees its own position.
Project page Paper & code soon
ICML 2026
LAGEA: Language Guided Embodied Agents for Robotic Manipulation
A vision–language model reflects on each failed episode in structured language, and the reflection becomes time-localised reward shaping for robot manipulation.
AAAI 2026
T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion
T3Time reads a series three ways (in time, in frequency, and as a language prompt) and lets the forecast horizon decide how to weigh them.
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