Ghost Fighter

Inspiration

Humanoid robot sports are coming.

Projects like Ultimate Bots and Ghost Trials make it possible to capture human movement and deploy it onto humanoid robots. But we noticed a missing step in the workflow:

How do creators know whether a move is actually robot-ready before it reaches hardware?

A punch combo might look amazing on camera but be unstable, inefficient, or difficult for a humanoid robot to execute safely. Today, most motion-capture workflows stop at visualization. We wanted to build the layer that evaluates, coaches, and stress-tests human-created movements before deployment.

That's how Ghost Fighter was born.

Our vision is simple: treat human movement like a draft pick for a robot athlete. Every move should be scored, analyzed, improved, and battle-tested before it ever reaches a real robot.

What it does

Ghost Fighter transforms human-created motions into structured robot skills.

A creator can export a motion from Ultimate Bots Studio, and Ghost Fighter automatically:

Ingests SONIC trajectory data
Maps joints from IsaacLab ordering to MuJoCo / Unitree G1 ordering
Generates a structured move card
Scores robot readiness and deployability
Provides AI coaching feedback
Replays the motion on a simulated Unitree G1
Tests the move inside a robot-sports arena

Instead of viewing motion as a video, we treat it as a reusable robot skill.

Each move receives attributes such as:

Speed
Power
Smoothness
Balance Risk
Recovery
Deployability

We also generate coach-style feedback such as:

"Looks powerful, but widening your stance before commitment would improve balance and recovery."

During the hackathon we shipped two fully functioning skills:

Ghost Jab Combo
Block (including full SONIC defensive animation replay)

How we built it

We built a complete end-to-end Physical AI pipeline:

Human Motion → SONIC Export → Move Analysis → G1 Replay → Robot Sports Arena

Motion Processing

We ingest SONIC motion trajectories exported from UFB Studio and convert them into a format compatible with the Unitree G1 humanoid model.

To achieve this, we implemented:

Joint remapping between IsaacLab and MuJoCo conventions
Motion playback systems
Move-card generation and scoring
Skill metadata extraction

AI Agents

We introduced specialized agents that each serve a distinct role:

Coach Agent

Provides actionable feedback
Suggests improvements for robot safety and execution quality

Judge Agent

Evaluates whether a move is deployable
Produces readiness assessments

Announcer Agent

Generates live fight commentary
Creates the feeling of a robot-sports broadcast

For voice commentary, we integrated Deepgram to bring matches to life.

Arena Simulation

We built a lightweight robot-sports arena where players can test moves in real gameplay scenarios.

The arena includes:

Health
Stamina
Balance
Positioning
Range checking
Blocking mechanics
Recovery windows

Moves can be evaluated not just visually, but competitively.

We also implemented multiplayer support so teammates can join the same arena and test move cards against one another.

Challenges we ran into

The hardest challenge was building a reliable bridge between motion-capture data and humanoid robot playback.

Different robotics systems use different joint ordering conventions, so motion data could not simply be imported and replayed directly. We had to carefully map and validate trajectories between SONIC exports, simulation environments, and the Unitree G1 model.

Another challenge was deciding where AI should actually add value. Rather than using LLMs everywhere, we focused on specialized agents that provide meaningful coaching, judging, and commentary while keeping gameplay deterministic and responsive.

Finally, we had to balance two very different experiences:

A serious Physical AI evaluation platform
A fun robot-sports game

Designing a system that could be both useful and entertaining within 48 hours was one of the most rewarding parts of the project.

Accomplishments that we're proud of

Built a complete human-motion-to-robot pipeline
Replayed real SONIC trajectories on a Unitree G1 model
Generated structured robot skill cards
Developed AI coaching and judging systems
Created a multiplayer robot-sports arena
Integrated live AI-powered commentary
Delivered an end-to-end working demo in under 48 hours

Most importantly, we transformed raw human motion into something measurable, coachable, and competitive.

What we learned

Building Ghost Fighter taught us that the future of Physical AI is not just about making robots move.

It's about translating messy human creativity into structured robot behavior.

A useful robot-sports ecosystem needs more than robots. It needs creators, coaches, evaluation tools, testing environments, and feedback loops.

We believe Ghost Fighter represents an early version of that creator ecosystem.

What's next for Ghost Fighter

This hackathon gave us the foundation for a much larger platform.

Next steps include:

Direct Coach and Judge integration into the move-builder workflow
Custom fighter loadouts
Persistent player progression and leaderboards
Redis-powered move memory and analytics
Automated highlight reel generation
Larger-scale multiplayer tournaments
Hardware validation workflows for real humanoid robots

Long term, we envision Ghost Fighter becoming a creator platform for robot athletes.

Anyone should be able to design a move, receive AI coaching, test it in competition, and eventually watch it perform on real humanoid hardware.

One-line summary

Ghost Fighter turns human movement into ranked, coachable, battle-tested robot skills—the layer between human creativity and the future of humanoid robot sports.