Methods and systems for food preparation in a robotic cooking kitchen
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Solution Overview
Problem
Current robotic systems lack the capability to replicate complex culinary tasks with the same precision and quality as human chefs, limiting the availability of gourmet dishes outside professional kitchens and requiring significant effort for consumers to access diverse cuisines.
Innovation Solution
A robotic cooking system equipped with multimodal sensing systems, including cameras, lasers, and human-motion capture technology, records and replicates a chef's movements, allowing for precise execution of recipes using robotic arms and sensors to control cooking parameters like temperature and time, enabling the preparation of gourmet dishes in home kitchens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robotic systems use pre-programmed trajectory execution to replicate chef movements, then the robot can faithfully execute motion commands without deviation, but the system lacks the capability to adapt to real-time cooking conditions and achieve gourmet quality
Solution Approach 1:
The system incorporates multiple sensors (cameras, lasers, motion capture) that continuously monitor cooking conditions and provide real-time feedback to the control system. This feedback loop enables the robotic system to detect deviations from the reference trajectory and automatically adjust its movements, temperatures, and timing to maintain gourmet quality standards while adapting to actual cooking conditions.
Solution Approach 2:
The robotic system transitions from static pre-programmed trajectories to dynamic adaptive execution. The reference trajectory serves as a guide rather than a rigid constraint, allowing the robot to dynamically adjust its motion paths, speeds, and forces based on real-time sensor data about ingredient states, cooking progress, and equipment conditions.
2Manufacturing precision
If a robotic system uses multiple sensors and motion capture technology to record and replicate chef movements, then it can achieve precise replication of cooking techniques, but the device complexity increases significantly
Solution Approach 1:
The complex sensing and control system is divided into modular functional components: motion capture subsystem, visual inspection subsystem, temperature monitoring subsystem, and control execution subsystem. Each module independently captures or monitors a specific aspect of the cooking process and transmits data to the central controller, which integrates all inputs to generate coordinated robotic actions. This segmentation reduces overall system complexity while maintaining high replication accuracy.
Solution Approach 2:
The system introduces a reference trajectory as an intermediary representation of the chef's cooking technique. Instead of directly copying complex human movements, the system first captures the essential cooking parameters and generates a simplified reference trajectory that encodes the key aspects of the technique. This intermediary model serves as a bridge between the complex sensing system and the robotic execution system, reducing the complexity of real-time control while preserving cooking accuracy.
3Extent of automation
If conventional robotic systems follow taught trajectories without deviation, then they can execute tasks automatically, but they cannot accommodate variations in ingredient properties or cooking conditions
Solution Approach 1:
The robotic system performs self-adjustment based on real-time sensor feedback. When deviations from the reference trajectory are detected (such as variations in ingredient texture, moisture content, or cooking progress), the system automatically modifies its own motion commands, heating parameters, and timing without requiring human intervention. This self-service capability maintains high automation while enabling adaptation to ingredient and condition variations.
Solution Approach 2:
Multiple sensors continuously monitor cooking conditions and compare actual states against the reference trajectory. The control system processes this feedback information and generates real-time corrections to the robotic actions, enabling the system to maintain automated execution while adapting to variations in ingredient properties and cooking conditions.
Data Source
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AI summary
The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.