Robotic Manipulator Placement Control via Reversed Retrieval Trajectories
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Solution Overview
Problem
Existing robotic manipulators face challenges in developing effective object placement policies due to the complexity of tasks like object tracking, scene understanding, and motion planning, which require substantial human effort and time to collect diverse expert demonstrations.
Innovation Solution
An electronic device and method for controlling robotic manipulators using a data collection system that includes a vision sensor and tactile sensor to autonomously collect training data, utilizing a self-supervised learning process to generate a control policy model for object placement, enabling robotic manipulators to learn from object retrieval trajectories and convert them into placement trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If imitation learning is used to learn end-to-end vision-based control policies from expert demonstrations, then the approach is simple and practical, but a substantial human time and effort is necessary to collect expert demonstrations
Solution Approach 1:
The system uses self-supervised learning where the robotic manipulator autonomously collects training data by performing object retrieval tasks itself, without requiring human expert demonstrations. The system generates its own training data by observing its own movements and sensor inputs during autonomous operation
Solution Approach 2:
Instead of collecting placement trajectories directly, the system collects object retrieval trajectories and reverses them to generate placement trajectories. This inversion approach allows autonomous data collection by having the robot perform retrieval tasks and then mathematically reversing the recorded trajectories for training the placement policy
2Adaptability or versatility
If diverse expert demonstrations are collected to cover various factors such as backgrounds, lighting, and object types, then the training data comprehensiveness is improved, but the human effort and time required increases substantially
Solution Approach 1:
The robotic manipulator autonomously collects diverse training data by independently exploring various objects, backgrounds, and lighting conditions in the environment. The system performs self-supervised learning by recording its own sensor inputs and actions across diverse scenarios without human intervention
Solution Approach 2:
The system reverses object retrieval trajectories to generate placement trajectories, automatically creating diverse training data by inverting recorded movements. This approach naturally captures diverse scenarios including different objects, backgrounds, and environmental conditions that the robot encountered during autonomous retrieval tasks
Data Source
AI summary
A method for controlling a robot including a manipulator is provided. The method may include: acquiring an image of a scene including a target object; inputting the image into a control policy model to obtain an object placement pose of the manipulator, as an output of the control policy model, wherein the control policy model is generated using data collected by a data collection system that is configured to acquire an object retrieval trajectory by observing a robot movement for object retrieval, and reverse the object retrieval trajectory into an object placement trajectory; and commanding the robot to position the manipulator according to the object placement pose, to place the target object at a designated location.