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A physics-based multiplayer cooking game built in Unity where chaos meets culinary challenges.

Control ragdoll characters to grab ingredients, operate kitchen stations, complete orders, and deliver dishes—all while managing environmental hazards like conveyor belts, explosive drones, and speeding trains. Features local multiplayer support for up to 4 players with skill-check mini-games and recipe-driven cooking mechanics.

Perfect for chaotic couch co-op sessions with friends!

Michelin Mayhem

A chaotic 3D cooperative multiplayer cooking game built in Unity featuring physics-based ragdoll character control and dynamic kitchen mayhem.

Gameplay

Work together with up to 4 players in local multiplayer to:

  • Grab & Cook: Use dual-hand physics controls to pick up ingredients and manage multiple cooking stations
  • Complete Orders: Prepare dishes following recipes and deliver them before time runs out
  • Survive the Chaos: Dodge trains, avoid explosions, and navigate conveyor belts while cooking
  • Master Skills: Complete timing-based skill check mini-games for precision cooking

Features

  • Ragdoll Physics System: Full-body character control with realistic physics interactions
  • Multiple Kitchen Stations: Cooking stations, pizza ovens, transformers, and assembly counters
  • Dynamic Hazards: Trains, drones, conveyor belts, and environmental fires
  • Recipe-Driven Cooking: Combine ingredients to create various dishes
  • Local Multiplayer: Support for 2-4 players with gamepad controllers
  • Skill-Based Mechanics: Timing challenges and precision-based cooking tasks

Built With

  • Unity (URP/HDRP)
  • C#
  • Unity Input System
  • Cinemachine
  • Physics-based character controllers

Game Flow

MainMenu → Lobby (character customization) → Tutorial → Level Gameplay → Awards Screen

About

A chaotic 3D cooperative cooking game where up to 4 players use ragdoll physics to grab, cook, and deliver dishes while dodging trains, drones, and explosions. Built in Unity with C#.

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  • C# 86.3%
  • HLSL 8.5%
  • ShaderLab 3.6%
  • HTML 1.6%