M A HAFIZ

Results & Impact

Erlangen, Germany • Robotics Simulation & Control • Sim2Real

Proof that my systems work (not just “cool demos”)

This page highlights measurable outcomes across my robotics work: simulation fidelity, stable control, task success, and repeatable evaluation. Replace the placeholder numbers with your exact metrics when ready.

Tip: Recruiters scan this page first. Keep metrics concrete: success-rate, failures, drift, time-to-complete, stability, and reproducibility.
See Projects See Architecture Download CV Contact

X%

Task Success Rate (Pick/Place / Manipulation)

Replace with your measured success rate (e.g., over N episodes).

±X mm

Positioning Accuracy (end-effector / grasp)

Use mean ± std or max error during approach / grasp.

X hrs

Evaluation Automation (CI regression gating)

Time saved per week via automated rollouts + SHIP/BLOCK decisions.

Robot Eval Platform — Regression Gating Outcomes

  • Automated rollouts into metrics + videos + reports for every candidate run.
  • Baseline vs candidate comparison with a clear SHIP / BLOCK decision.
  • CI-friendly outputs for repeatability and quick reviews.
FastAPIReactPostgreSQL MinIO/S3CI/CD
Add here: a screenshot of your dashboard summary page + an example metrics.json snippet.

Metrics you should report (recommended)

  • Success rate, time-to-complete, safety violations
  • Stale command ratio, command rate, NaN/invalid states
  • Robustness: p10 / p50 / worst-case metrics
  • Video + plots stored as artifacts
Add here: a “Before vs After” table once you have baseline + candidate results.

Franka Panda — Sim2Real Control & Validation

  • Gesture-based Cartesian control validated in MuJoCo and transferred to real FR3.
  • Focus on stable behavior: smooth motion, safe limits, and predictable response.
  • Designed for user-centered HRI: minimal gestures with a menu-based control flow.
MuJoCoCartesian ControlIK/Jacobian EMG/IMUHRI
Add here: a photo of the real robot test or a short GIF/video link.

Suggested quantitative checks

  • Pose tracking error (translation/rotation) while moving along X/Y/Z
  • Orientation drift during translation
  • Overshoot / settling time after step commands
  • Repeatability across runs and users
Add here: a simple plot: desired vs actual end-effector trajectory.

What I optimize for

  • Measurable progress (metrics-first, reproducible experiments)
  • Stability & safety (limits, smooth control, robust behavior)
  • Deployment readiness (CI-style checks and clear failure modes)
  • Fast iteration (tight loop: simulate → evaluate → improve)