Hi, I'm Maxwell Ma
Graduate researcher at Rice University. I build intelligent robotic systems at the intersection of physics-informed learning, visuo-tactile perception, and sim-to-real transfer.
Research Projects
Vision–Tactile Mass Priors with Online
Physics-Informed Grip Optimization
for Compliance-Robust Manipulation
A staged grasp-and-lift framework performing online physical correction after contact: an IL base policy handles geometry-feasible approach; a vision model predicts mass priors pre-contact; a tactile micro-lift estimates realized mass and mismatch Δm; and a compact PINN outputs grip force by explicitly penalizing friction-cone violation, force-bound violation, and compliance-related pressure concentration. Implemented on a torque-controlled Franka Emika Panda with RGB-D + capacitive tactile arrays on Jetson AGX Orin.
LeRobot × Open-PI Integration —
Instruction-Conditioned VLA
for Tabletop Manipulation
End-to-end research pipeline for Vision-Language-Action policies targeting tabletop manipulation. The project spans the full ML development cycle: real-robot data collection → cross-dataset training → Isaac Sim evaluation → sim-to-real feedback iteration, bridging the LeRobot framework with Open-PI for scalable instruction-conditioned robotic control.
Data Collection
ROS 2 teleoperation pipeline recording synchronized RGB-D frames, proprioceptive states, and language annotations. Custom episode filtering and quality validation before dataset ingestion.
Training — LeRobot × Open-PI Adaptation
Adapted Open-PI dataset format for instruction-conditioned policy training within LeRobot. Multi-task training in PyTorch and JAX; cross-domain fine-tuning to bridge internet-scale data with lab demonstrations.
Evaluation — NVIDIA Isaac Sim Benchmarking
Systematic benchmark of open-source VLA models across standardized tabletop tasks. Metrics: success rate, trajectory length, latency, and language-instruction following accuracy under varied scene configurations.
Feedback Iteration — Sim-to-Real Gap Analysis
CI-backed architecture with reproducible training/eval scripts. Systematic failure-mode analysis; targeted data augmentation and domain randomization to close the sim-to-real gap iteratively.
Contact
Open to research collaborations, lab discussions, and opportunities in robotics & AI.
Work Demonstration
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Industry Experience
Senior Strategy AI Algorithm Engineer
Beijing, China