RL fine-tuning for VLA-based long-horizon task failure recovery.
VLA Models / RL Fine-Tuning / Mobile Manipulation
Haodi (Woody) Hu
Ph.D. graduate from the University of Southern California currently focused on vision-language-action models, reinforcement learning fine-tuning, and mobile manipulation for long-horizon task execution.
I am currently a Research Scientist Intern at MERL, where I work on RL fine-tuning for VLA models to improve failure recovery in long-horizon mobile manipulation tasks. My broader research grew out of USC RoboLAND and spans robot learning, embodied reasoning, granular loco-manipulation, and multi-robot coordination, where I worked with Professor Feifei Qian and collaborated with Professor Daniel Seita.
Accepted to CoRL 2025.
Long-horizon reasoning, recovery, and policy adaptation for embodied agents.
Research Direction
Advancing VLA-guided mobile manipulation with reinforcement learning
My current research emphasizes VLA models and RL fine-tuning for mobile manipulation, especially how embodied agents recover from failures and continue long-horizon tasks under uncertainty.
Core areas
Vision-Language-Action Models
Reinforcement Learning
Mobile Manipulation
Long-Horizon Recovery
Robot Learning
Embodied Reasoning
Policy Adaptation
Multi-Robot Systems
Recent Updates
News and momentum
Research Scientist Intern at MERL focusing on RL fine-tuning for VLA failure recovery in long-horizon tasks.
Paper accepted to the 9th Conference on Robot Learning: Granular Loco-manipulation.
Worked as a Machine Learning Engineer intern in the Data Science group at SanDisk.
Co-organized the 5th workshop on representations and manipulating deformable objects at ICRA 2025.
Presented research on legged robot loco-manipulation and obstacle-aided locomotion at ICRA 2025.
T-RO paper accepted on obstacle-aided trajectory control through sequential gait composition.
Selected Publications
Recent projects, papers, and videos
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Learning Granular Media Avalanche Behavior for Indirectly Manipulating Obstacles on a Granular Slope
A learning-based framework for predicting granular avalanche behavior to support indirect manipulation on sandy slopes.
Method for Detecting Micron Cracks on a Magnetic Rotor Surface Based on a Support Vector Machine
Teaching, Mentoring, and Service
Academic activities beyond publications
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Teaching experience
- Robot Mobility (EE599) — Teaching Assistant, Fall 2022
- A Computational Introduction to Deep Learning (EE541) — Teaching Assistant, Spring 2023, Fall 2023, Spring 2024
- MOS VLSI Circuit Design (EE477L) — Teaching Assistant, Fall 2024, Spring 2025
Awards and service
- USC Viterbi CURVE Mentor Award, 2022–2025
- USC Viterbi Ph.D. Student Fellowship Award, 2021
- NEFU Excellent Graduates Award, 2019
- Workshop co-organizer for ICRA 2025 deformable objects workshop
Mentoring
- Supervised students including Luke Cortez, Jerry Wu, Seojoon Kwon, Tian Xie, and Brendon Lee.
- Served as Ph.D. mentor in the USC CURVE program across multiple cohorts from 2022 to 2025.
- Supported student researchers through project mentorship and conference preparation.
