Robotic Object Placement Using Visual ML Feedback
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Solution Overview
Problem
Robots programmed to place objects in fixed locations fail in dynamic or varying environments without explicit reprogramming, as they cannot adapt to changes in object or bin locations.
Innovation Solution
A machine learning model, such as a deep neural network, is trained to process images of the robot's environment and end effector, generating predictions for successful object placement based on candidate actions, allowing continuous adjustment and release of the object in a target location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robot is programmed to place objects in fixed locations, then the robot can perform placement tasks reliably in static environments, but the robot fails to adapt when the environment changes or objects/bins are moved to different locations
Solution Approach 1:
The system transitions from static preprogrammed placement locations to dynamic adaptive placement locations. The machine learning model continuously learns from visual feedback and environmental changes, allowing the robot to adapt its placement behavior in real-time based on the current environment state, thereby resolving the contradiction between reliability and adaptability
Solution Approach 2:
The system implements a feedback loop where the robot captures images of the environment, processes them through a machine learning model, and uses the predictions to adjust its placement actions. This closed-loop feedback mechanism enables the robot to adapt to environmental changes while maintaining reliable placement performance
2Device complexity
If a robot uses preprogrammed fixed locations for object placement, then the control system is simple and deterministic, but the robot cannot handle dynamic environments without explicit reprogramming
Solution Approach 1:
The system replaces traditional mechanical control programming with a machine learning-based perceptual system. Instead of using complex if-then programming rules to handle every possible environmental variation, the system uses image processing and neural network predictions to automatically adapt to new environments, reducing control complexity while improving adaptability
Solution Approach 2:
The robot performs self-learning and self-adjustment through the machine learning model. The system automatically adapts to environmental changes without requiring external reprogramming or human intervention, enabling the robot to serve itself in dynamic environments while maintaining simple control architecture
Data Source
AI summary
Training and/or use of a machine learning model for placement of an object secured by an end effector of a robot. A trained machine learning model can be used to process: (1) a current image, captured by a vision component of a robot, that captures an end effector securing an object; (2) a candidate end effector action that defines a candidate motion of the end effector; and (3) a target placement input that indicates a target placement location for the object. Based on the processing, a prediction can be generated that indicates likelihood of successful placement of the object in the target placement location with application of the motion defined by the candidate end effector action. At many iterations, the candidate end effector action with the highest probability is selected and control commands provided to cause the end effector to move in conformance with the corresponding end effector action. When at least one release criteria is satisfied, control commands can be provided to cause the end effector to release the object, thereby leading to the object being placed in the target placement location.