Robot Action Sequence Prediction for Changing Environments
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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 the need to adapt to changing environments, as existing technologies often require explicit learning of action sequences without considering environmental changes.
Innovation Solution
An environment-conditioned action sequence prediction model using a convolutional neural network and sequence-to-sequence model processes vision data to predict sequences of actions, allowing robots to adapt to varying environments and perform tasks by learning dependencies and transitions from demonstration videos, with action symbols representing abstract actions that can be executed by individual policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot is programmed to perform specific tasks with fixed action sequences, then task execution is reliable and predictable, but the robot cannot adapt to environmental changes such as objects in different positions or states
Solution Approach 1:
The patent segments the complex task planning problem into two distinct components: (1) an action sequence prediction model that determines the ordered list of actions needed to achieve a goal, and (2) individual action prediction models that execute each specific action. This segmentation allows the system to handle environmental variability at the sequence planning level while maintaining reliable execution at the individual action level, thereby improving adaptability without overwhelming complexity.
Solution Approach 2:
The action sequence prediction model serves as an intermediary between the high-level task goal and the low-level individual actions. It takes the goal and current environment state as input, predicts the necessary sequence of actions, and passes this sequence to individual action prediction models for execution. This intermediary layer enables the robot to adapt to environmental changes by generating appropriate action sequences while maintaining the reliability of individual action execution.
2Adaptability or versatility
If the robot learns action sequences explicitly without considering environmental changes, then training is simpler and faster, but the robot fails to respond to variations in object positions and environment states
Solution Approach 1:
The patent implements dynamics by making the action sequence prediction adaptive to environmental conditions. The model takes the current environment state (including object positions and states) as input and dynamically generates the appropriate action sequence. This allows the robot to respond to environmental variations in real-time, improving adaptability while the efficient neural network architecture minimizes the time penalty for this adaptive planning.
3Reliability
If the robot uses a complex model to predict action sequences based on environmental conditions, then task completion success rate improves, but computational resources and model complexity increase
Solution Approach 1:
The patent segments the complex prediction task into two specialized models: the action sequence prediction model that handles high-level planning and sequence determination, and individual action prediction models that handle low-level execution details. This segmentation improves reliability by ensuring each model specializes in its specific function, while the modular structure manages complexity through clear separation of concerns and independent training of each component.
Data Source
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.