A CoRL 2026 submission on VLM-guided reward compilation for VLA policies.
VLA Models / Reinforcement Learning / Mobile Manipulation
Haodi (Woody) Hu
Ph.D. graduate from the University of Southern California currently focused on vision-language-action models, world-action models, reinforcement learning, and mobile manipulation systems for robust embodied autonomy.
I am currently a Research Scientist Intern at MERL, where I work on ReCoVLA: a project connecting VLM-guided reward design, residual RL, and VLA policies for mobile manipulation. 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.
Mobile manipulation, long-horizon reasoning, tactile sensing, and policy adaptation.
Research Direction
Building VLA and RL systems for mobile manipulation
My current research emphasizes language-conditioned robot policies, reinforcement learning, and sensory feedback for manipulation in unstructured settings, from mobile manipulation to emerging dexterous tactile manipulation.
Core areas
Vision-Language-Action Models
Reinforcement Learning
Mobile Manipulation
World-Action Models
Long-Horizon Manipulation
Robot Learning
Embodied Reasoning
Policy Adaptation
Multi-Robot Systems
Recent Updates
News and momentum
Submitted ReCoVLA, a VLM-guided reward compilation framework for VLA policies, to CoRL 2026.
Research Scientist Intern at MERL focusing on VLA models, reinforcement learning, and mobile manipulation.
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
Recent work across VLA models, reinforcement learning for manipulation, dexterous tactile manipulation, granular loco-manipulation, obstacle-aided navigation, and multi-robot systems.
Spotlight paper
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
A VLA/RL framework that keeps the base policy frozen, uses Qwen3-VL to infer structured task context, and compiles stage-gated rewards. ReCoVLA improved simulation success from 36.7% to 66.7% and achieved 61.7% success on a physical Fetch robot.
TacSushi: Learning Sushi Making with a Dexterous Tactile Manipulator and World Models
An emerging Shadow Hand project on tactile sensing, teleoperation, and dexterous manipulation of deformable objects for sushi-making tasks, including hand-tracked teleoperation, soft-object handling, and cake manipulation.
Granular Loco-manipulation: Repositioning Rocks Through Strategic Sand Avalanche
A research direction in which legged robots use learned models of granular dynamics to reshape terrain and indirectly manipulate obstacles.
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
- IEEE Access Exceptional Reviewer Recognition, 2026
- 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.
