Robot Action Sequence Prediction for Adaptive Task Planning
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Solution Overview
Problem
Robotic systems face challenges in performing complex object manipulation tasks due to the complexity of task planning over long horizons and adapting to changing environments, as existing technologies struggle to learn dependencies and transitions across subtasks effectively.
Innovation Solution
An environment-conditioned action sequence prediction model using a convolutional neural network and sequence-to-sequence model processes vision data to predict and adapt sequences of actions, allowing robots to interactively adjust to environmental changes and recover from failures by learning from demonstrations and visual observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot uses traditional programming to perform tasks, then it can execute specific manipulations reliably, but it cannot adapt to changes in the environment such as objects in different positions or different initial states
Solution Approach 1:
The patent segments complex robotic tasks into sequences of subtasks, where each subtask corresponds to a specific action (e.g., pick object, move object, place object). This segmentation allows the robot to handle complex tasks through manageable steps while maintaining adaptability to environmental variations.
Solution Approach 2:
The system performs preliminary learning of task sequences from demonstrations before actual execution. By pre-learning the sequence of actions needed for various tasks from video demonstrations, the robot builds a knowledge base that enables adaptive execution without requiring complex real-time planning.
2Adaptability or versatility
If a robot learns task sequences from demonstrations, then it can generalize to new situations, but it requires complex processing to learn dependencies and transitions across subtasks
Solution Approach 1:
The system uses feedback from video demonstrations to learn task sequences. By analyzing demonstrated tasks and extracting action sequences, the system automatically learns the dependencies and transitions between subtasks without explicit programming, reducing the complexity of learning while improving generalization.
Solution Approach 2:
The patent introduces an intermediate representation layer between raw video demonstrations and robot execution. This intermediate layer processes demonstrations into structured task sequences, making the learning process more efficient and the dependencies between subtasks more manageable.
3Productivity
If a robot executes long-horizon plans, then it can complete complex multi-step tasks, but it struggles to maintain task completion when encountering unexpected environmental changes
Solution Approach 1:
The system dynamically adjusts task execution based on observed environmental changes. Rather than rigidly following pre-planned sequences, the robot can modify its action sequence in real-time based on what it observes, allowing it to complete long-horizon tasks while adapting to unexpected changes.
Solution Approach 2:
The robot performs self-correction by monitoring its own execution and the environment. When deviations occur from the planned sequence, the system automatically adjusts its behavior to recover and complete the task, reducing reliance on external intervention and improving reliability.
Data Source
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AI summary
Training and/or using a machine learning model for performing robotic tasks is disclosed herein. In many implementations, an environment-conditioned action sequence prediction model is used to determine a set of actions as well as a corresponding particular order for the actions for the robot to perform to complete the task. In many implementations, each action in the set of actions has a corresponding action network used to control the robot in performing the action.