Predicate-Based Symbolic State Estimation for Contact-Rich Manipulation
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Solution Overview
Problem
Conventional state estimation techniques for robotic manipulation are highly susceptible to noise and require vast amounts of training data, lacking generalizability to other tasks.
Innovation Solution
Representing symbolic states as a collection of predicate values using intermediate predicate classifiers, which are trained with smaller datasets and are modular, allowing for adaptability and robustness to noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional state estimation techniques are used to predict ground truth state from noisy observations, then continuous state estimation can guide robot motion, but the system becomes highly susceptible to noise and requires vast amounts of training data
Solution Approach 1:
The patent segments the state estimation problem into discrete symbolic states rather than continuous estimation. By dividing the state space into distinct symbolic categories (e.g., grasping, manipulating, placing), the system avoids the noise sensitivity of continuous estimation while requiring less training data for classification.
Solution Approach 2:
The patent introduces symbolic states as an intermediary layer between raw sensor observations and robot control decisions. This intermediary representation filters out noise from continuous observations by mapping them to discrete symbolic categories, reducing the training data burden while maintaining estimation accuracy.
2Measurement precision
If conventional state estimation techniques are trained on specific tasks, then they achieve accuracy for those tasks, but they become non-generalizable to other tasks
Solution Approach 1:
The patent creates a universal symbolic state representation that can apply across multiple manipulation tasks. By defining a set of task-agnostic symbolic states (grasping, manipulating, placing) that can describe different physical interactions, the system achieves both accuracy for specific tasks and generalizability to new tasks without retraining.
Solution Approach 2:
The patent makes the state estimation system dynamic and adaptable by using symbolic states that can be flexibly applied to different task contexts. The same symbolic framework can dynamically adjust to new tasks through composition rather than requiring fixed, task-specific training, enabling versatility while maintaining precision.
3Loss of information
If high-dimensional sensor observations are processed directly to determine symbolic states, then complete information is utilized, but the system becomes susceptible to noise and requires extensive training data
Solution Approach 1:
The patent extracts essential symbolic information from high-dimensional sensor observations by mapping continuous observations to discrete symbolic states. This extraction process filters out noisy details while preserving the essential semantic meaning needed for task execution, improving reliability without losing critical information.
Solution Approach 2:
The patent creates a symbolic copy or abstraction of the physical state represented by high-dimensional sensor data. This symbolic representation captures the essential task-relevant information while discarding noisy sensory details, achieving robustness to sensor noise while maintaining information utility for control decisions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a robot using symbolic states. One of the methods includes receiving a definition of a task having multiple task states, wherein each task state is associated with a different respective control policy; executing the task using an initial control policy associated with an initial task state; during execution of the task, continually generating, from sensor data, a prediction of the task state of the task; and upon determining that a transition to a different task state has occurred, transitioning the robot to a different control policy associated with the different task state.