Robot Control Policy Training With Semi-Supervised Trajectories
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Solution Overview
Problem
Existing diffusion models for robot control require costly expert-gathered state-action sequences for training, making data collection challenging.
Innovation Solution
Utilize less costly training data, such as state sequences without action labels, to train goal-conditioned trajectory and action prediction models, allowing semi-supervised learning of robot control policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert trajectory data with state-action sequences is used for training diffusion models, then the robot control policy accuracy is improved, but the data collection cost and time increase significantly
Solution Approach 1:
The training data is segmented into two types: expert trajectory data with state-action sequences for high-accuracy training, and less costly state sequences without action labels for supplementary training. This segmentation allows the system to leverage both high-quality expert data and abundant inexpensive data, resolving the contradiction between accuracy and data collection efficiency
Solution Approach 2:
The patent applies partial supervision by using only state sequences without full action labels for part of the training data. This partial action approach allows leveraging abundant inexpensive data while maintaining training effectiveness through the combination with expert data, reducing overall data collection time while preserving control policy accuracy
2Measurement precision
If expert trajectory data with state-action sequences is used for training diffusion models, then the robot control policy accuracy is improved, but the computational resources required increase
Solution Approach 1:
The training dataset is segmented into expert trajectory data requiring full computational processing and less costly state sequences that can be processed more efficiently. This segmentation optimizes computational resource allocation by applying different processing strategies to different data types, maintaining accuracy while reducing overall energy consumption
Solution Approach 2:
The patent changes the supervision parameter from full state-action pairs to partial state-only sequences for portions of the training data. This parameter change reduces the computational burden per training sample while maintaining overall model accuracy through the semi-supervised learning framework
3Reliability
If expert trajectory data with state-action sequences is collected, then the training quality is improved, but the complexity of data collection processes increases
Solution Approach 1:
The data collection process is segmented into two streams: one collecting expert trajectory data with full state-action sequences through teleoperation, and another collecting less costly state sequences through automated recording. This segmentation simplifies the overall data collection process by dividing it into manageable tasks with different complexity levels, maintaining training quality while reducing process complexity
Solution Approach 2:
The system implements self-service data collection by automatically recording state sequences from robot operations without requiring expert annotation for all data. This self-service approach collects abundant training data through simple automated processes, reducing the complexity of manual expert data collection while maintaining sufficient training quality
Data Source
AI summary
Implementations are provided for leveraging training data that is less costly to collect than state-action sequences to perform semi-supervised training of robot control policies. In various implementations, a first input prompt may be assembled with representations of an observed initial state of a robot and a goal state of the robot. The first input prompt may be processed using a goal-conditioned trajectory model to generate first output indicative of a sequence of predicted states to be reached by the robot between the observed initial and goal states. A second input prompt may be assembled to include representations of the sequence of predicted states. The second input prompt may be processed using an action prediction model to generate second output indicative of a sequence of predicted actions to be performed by the robot to reach the sequence of predicted states.