Robotics & Simulation
I run simulations, cloud training, and agentic workflows to stress test unproven robotics ideas. Give me fuzzy problems and I’ll tell you if there is a solution worth chasing.
Demonstrations & Simulations
Hardened policy rollout imported from MuJoCo and rendered in Blender: smooth, contact-aware grasping and placement under realistic actuator lag and lighting.
* Note: Trajectories are 100% driven by the trained neural network policy exported from MuJoCo, rendered with photorealistic materials and lighting in Blender.
This closed-loop neural policy continuously observes multi-camera pixels and joint telemetry to adapt dynamically in real time.
θ1: -180.0°
θ2: -180.0°
x: 0.00 m
u: 0.0 N
SWING_UP · iLQR + TVLQR
The classic pendulum problem. The swing-up follows a trajectory solved offline with iLQR, tracked by a time-varying LQR; an infinite-horizon LQR catches it at the top. It's interactive if you wanna knock it over.
Physics simulation and applied machine learning to train and validate robotic control.
Headless physics simulation on cloud GPUs, distributed training loops, and telemetry. Everything you need to start making progress.
I'll collab with your hardware team to bring real-world test data back into the sim to close the sim-to-real gap.
Taking the Mamba-3 and Liquid CfC robot out of the tabletop sandbox and into a moving orchard platform in MuJoCo.
Continuing to improve the Mamba + LNN powered Franka Emika robot arm with parallelized cloud training, improved models, and closing the sim-to-real gap.
A systems view of continuous robot reinforcement learning: why control splits across a 20 Hz policy loop and a 500 Hz PD loop, how GAE trades bias for variance, the truncation bug that quietly biases every late-episode advantage, what the PPO clip does and does not constrain, and which metrics to read when a run goes bad.
My journey in robotics started from the lowest level up—making LEDs blink using assembly code on microcontrollers. I was one of the first 30 students to graduate from Texas A&M with a degree in Mechatronics.
After four years engineering distributed backend systems at Amazon AWS, I came back to my robotics roots. Most of my time now goes to control theory and physics simulation in MuJoCo. Lately I've been tinkering with reinforcement learning and small robotics-friendly models like Mamba 3 and Liquid Neural Nets.
Read My Full StoryServices
AI & Sim
Modern physics engines and applied machine learning, used to train and control robots before they meet the real world.
Fleet Data
Cloud architecture for getting data off the robot and back down again, built with the rigor of scaling distributed systems at AWS.
Interfaces
Operator dashboards and teleop interfaces for your fleet, from geospatial mapping to on-device inference and RAG-integrated tooling.
Consultation
A catch-all for odd mechatronics problems, embedded debugging, or an honest second opinion from someone who has built the hardware.
Estimates only. These are baseline prices and timelines for typical engagements, shared for transparency and budget planning. They aren't guaranteed final amounts, and complex or custom requirements will change the scope.
| Service / Deliverable | Category | Estimated Pricing | Expected Turnaround |
|---|---|---|---|
| Simulation Feasibility Audit | AI & Sim | $1,500 - $2,500 | ~1 Week |
| MuJoCo Proof of Concept | AI & Sim | Starts at $8,000 | Starts at 3 Weeks |
| Cloud Architecture Review | Fleet Data & Telemetry | $2,500 | ~1-2 Weeks |
| Scalable Data Pipeline Build | Fleet Data & Telemetry | Starts at $15,000 | Starts at 4-6 Weeks |
| Fractional Cloud Architect | Fleet Data & Telemetry | $6,000 - $9,000 / mo | Ongoing |
| LLM / RAG Integration MVP | Interfaces & AI | Starts at $5,000 | Starts at 2-3 Weeks |
| Full-Stack Application MVP | Interfaces & AI | Starts at $12,000 | Starts at 4-8 Weeks |
| 1-on-1 Strategy / Debugging | Consultation | $250 - $350 / hr | Bookable within 48h |
If you have a project that fits into these categories, or if you have a custom request, let's get in touch.
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