Adaptive Robot Stacking Planning for Object-Invariant Tasks
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Solution Overview
Problem
Conventional task and motion planning (TAMP) systems for object-invariant stacking operations rely on object-specific preconditions that require manual encoding, leading to inefficiencies and human errors, and fail to adapt to diverse objects effectively.
Innovation Solution
An adaptive task and motion planning (ATAMP) system that learns and generates new preconditions using a virtual Discrete Action Space (DAS) and an n-armed bandit problem, allowing a robotic agent to adapt to new objects by iteratively refining its action model based on visual clues and rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object-specific preconditions are manually encoded for TAMP, then task execution reliability is improved, but device complexity and manual effort increase tremendously
Solution Approach 1:
The system performs self-learning by automatically generating preconditions through interaction with the environment and reinforcement learning, eliminating the need for manual encoding. The robotic agent learns task-specific preconditions autonomously through trial and error, adapting to different objects and scenarios without human intervention.
Solution Approach 2:
Manual encoding of preconditions is replaced with an automated machine learning system. The reinforcement learning agent automatically discovers and generates preconditions through environmental interaction, substituting the mechanical process of manual programming with an intelligent automated system.
2Productivity
If traditional TAMP with fixed preconditions is used, then task planning efficiency is maintained, but adaptability to diverse objects deteriorates
Solution Approach 1:
The precondition model transitions from static and fixed to dynamic and adaptive. The reinforcement learning agent continuously updates preconditions based on environmental feedback and new experiences, allowing the system to adapt to diverse objects while maintaining planning efficiency through learned patterns.
Solution Approach 2:
The system changes the parameters of preconditions dynamically based on the specific objects and scenarios encountered. Instead of using fixed preconditions, the reinforcement learning agent adjusts precondition parameters adaptively, enabling versatility across different objects while preserving task planning efficiency.
3Adaptability or versatility
If manual enhancement of preconditions is performed, then task coverage is improved, but time consumption and human errors increase
Solution Approach 1:
The system autonomously expands its task coverage by self-learning from environmental interactions. The reinforcement learning agent automatically discovers new preconditions and task variations without manual enhancement, eliminating time consumption and human errors associated with manual precondition expansion.
Solution Approach 2:
The system uses feedback from successful and unsuccessful task executions to automatically learn and expand preconditions. Through reinforcement learning, the agent receives rewards or penalties based on task outcomes, enabling autonomous improvement of task coverage without manual intervention or time loss.
Data Source
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AI summary
This disclosure relates generally to a method and system for adaptive task and motion planning (ATAMP) for object-invariant stacking operations. Traditional TAMP considering only the preconditions for execution of an object stacking task is challenging for performing all kinds of the object-invariant stacking operations The disclosed method adopts an action model for a new object and stacking is performed by drawing inferences and learning rewards using a virtual Discrete Action Space (DAS) based on a heuristically defined reward function. These inferences are utilized for identifying a plurality of new preconditions. Additionally, an efficient stacking position selection strategy is used for a n-armed bandit problem, which leads to fast convergence for performing the object-invariant stacking operations. A robotic agent repetitively interacts with an environment in real-time to adapt the action model for the new object. After adaptation, the robotic agent can perform the object-invariant stacking operations on objects with varying poses.