Robot Manipulation Planning With Symbolic Skill Composition
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Solution Overview
Problem
Current robot manipulation systems face challenges in adapting to varying environments and performing complex tasks due to the impracticality of preprogramming all possible skills and the inability to generalize from demonstrated actions effectively, as they require precise definitions of planning models and preconditions for each skill.
Innovation Solution
A method for planning object manipulation that involves learning manipulation skills through demonstration, generating symbolic abstractions using Hidden Semi-Markov Models and PDDL planning, and determining a concatenated sequence of skills to achieve a given goal specification, allowing for adaptation to changes in the environment and optimization of the manipulation trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If preprogramming all robot capabilities is attempted, then the robot can perform various tasks, but the device complexity and programming effort become impractical
Solution Approach 1:
The patent segments robot manipulation capabilities into discrete, learnable skills rather than attempting to preprogram all capabilities. Each skill is learned independently through demonstration and can be composed into complex tasks, making the system manageable and scalable without overwhelming programming complexity
Solution Approach 2:
The patent creates a universal planning framework that can handle multiple different manipulation tasks using a common set of learned skills. The symbolic planning model and skill composition mechanism provide multi-functionality, allowing the same system to perform various tasks without requiring separate preprogramming for each
2Adaptability or versatility
If skill training is performed whenever needed, then the robot can adapt to new tasks, but the ease of operation decreases due to inconvenient training requirements
Solution Approach 1:
The patent performs skill training in advance through demonstration recording before actual task execution. Skills are learned and stored in the planning model beforehand, so when a new task arises, the robot can quickly compose existing skills rather than requiring training at the moment of need, improving user convenience
3Reliability
If demonstrated manipulation is recorded and replayed, then the robot can reproduce actions, but the adaptability to environmental changes is lost
Solution Approach 1:
The patent introduces symbolic abstractions and a planning model as intermediaries between demonstrated actions and execution. Instead of directly replaying demonstrations, the system extracts symbolic representations of skills and uses a planning model to reason about their composition and adaptation to new situations, enabling both reliable reproduction and environmental adaptation
Solution Approach 2:
The patent enables parameter changes in the planning model based on environmental conditions. The symbolic planning framework allows modification of skill parameters and composition strategies according to the current state, enabling the robot to adapt demonstrated skills to varying environments while maintaining reliable action execution
4Measurement precision
If a detailed planning model with preconditions and effects is defined for each skill, then the task planning accuracy improves, but the device complexity increases due to the large variation of skills
Solution Approach 1:
The patent segments the planning model into a hierarchical structure with general skill definitions and specific task compositions. Rather than defining detailed preconditions and effects for every possible skill instance, the system uses a segmented approach where common patterns are defined once and reused, maintaining planning precision while reducing model definition complexity
Solution Approach 2:
The patent implements partial definition of planning models by focusing on essential preconditions and effects for skill composition rather than exhaustively defining all possible details. The symbolic planning framework uses sufficient but not excessive model detail, achieving adequate planning precision without the complexity of complete formal definitions for all skills
Data Source
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AI summary
The invention relates to a method for planning a manipulation task of an agent, particularly a robot, comprising the steps of: - Learning (S1) a number of manipulation skills (ah) wherein a symbolic abstraction of the respective manipulation skill is generated; - Determining (S3) a concatenated sequence of manipulation skills (ah) selected from the number of learned manipulation skills (ah) based on their symbolic abstraction so that a given goal specification (G) indicating a given complex manipulation task is satisfied; - Executing (S4) the sequence of manipulation skills (ah).