System and method for teaching a multi-arm robot to mimic human motions for making coffee and other beverages
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Solution Overview
Problem
Existing robotic systems struggle to perform beverage preparation tasks in open environments due to the constant movement of utensils and cookware, requiring predefined and hardcoded motions that cannot adapt to changing setups.
Innovation Solution
A multi-arm robot is taught to imitate two-handed beverage-preparation motions by recording and transforming motion trajectories using motion trackers on vessels, synchronizing and transforming these trajectories into the robot's end effector coordinates, and compensating for unwanted movements through a feedback loop and vision-guided adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic systems use predefined and hardcoded motions in a closed box environment, then the robot can complete beverage preparation tasks with fixed item placements, but the system cannot adapt when utensils and cookware are moved in realistic open environments
Solution Approach 1:
The system employs motion capture technology to record actual human barista movements and uses this recorded motion data as feedback to train and improve the robotic system's performance. The motion capture receiver continuously monitors and records trajectories, providing feedback that enables the robot to learn and adapt to different beverage preparation techniques and environmental conditions.
Solution Approach 2:
The system creates digital copies of human barista movements by recording motion trajectories with motion capture technology. These copied motion paths are then stored and replayed by the robotic system, allowing the robot to replicate complex human skills without requiring complex real-time decision-making algorithms.
2Ease of operation
If the robotic system operates in a closed box with fixed item placements, then programming is simplified with predefined motions, but locating utensils becomes challenging in realistic coffee shop environments where items are constantly moved
Solution Approach 1:
The system replaces traditional mechanical positioning and search mechanisms with optical motion capture technology. Instead of using sensors to actively search for utensils, the system uses optical tracking to record and reproduce the exact trajectories of human operators, thereby substituting mechanical location-finding with optical measurement and digital reproduction.
3Manufacturing precision
If motion trajectories are recorded for both hands during two-handed beverage preparation, then the robot can accurately replicate complex motions like latte-making, but the data processing and transformation complexity increases
Solution Approach 1:
The system segments the two-handed motion capture data into separate trajectories for each hand, allowing independent processing and analysis of each limb's movement. This segmentation enables the robotic system to process complex two-handed operations as coordinated sequences of simpler single-arm movements, reducing the overall computational complexity.
Solution Approach 2:
The system introduces motion capture receivers and trajectory transformation algorithms as intermediary components between the human operator and the robotic system. These intermediaries process the raw motion data, transform it into robot-compatible coordinate systems, and generate executable motion commands, thereby simplifying the interface between human skill and robotic execution.
Data Source
AI summary
Embodiments described herein provide various techniques and systems for teaching a multi-arm robot to imitate a two-handed beverage-preparation motion sequence performed by a human. In one aspect, a process for teaching a multi-arm robot to imitate the two-handed beverage-preparation motion sequence can begin by recording, in a teaching environment, two or more motion trajectories during the two-handed beverage-preparation motion sequence. The process then transforms the recorded two or more motion trajectories into a motion trajectory of an end effector of a first robotic arm. The process subsequently reproduces the two-handed beverage-preparation motion sequence by executing the transformed motion trajectory on an end effector of the first robotic arm.