Robot Task Planning With Affordance Maps for Multi-Step Assembly
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Solution Overview
Problem
Current approaches to autonomous robotic grasping and manipulation in dynamic environments are inefficient and limited to single-step operations, failing to effectively plan sequences of motions for complex tasks such as assembly, which requires multiple steps or a sequence of motions.
Innovation Solution
An autonomous system that includes a robot configured with object recognition, pose estimation, affordance analysis, decision-making, probabilistic task or motion planning, and object manipulation, capable of generating affordance maps and solving Markov decision problems to determine optimal motion sequences for fulfilling tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots learn skills through physical interactions in the real world, then the robot can acquire practical manipulation skills, but the process becomes time consuming, cost prohibitive, and dangerous
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical environment and objects, allowing the robot to learn and practice skills in a simulated digital replica rather than repeatedly interacting with physical objects. This virtual copying enables skill acquisition without the time consumption, cost, and safety risks of extensive physical trial-and-error interactions.
Solution Approach 2:
The system performs preliminary skill learning and practice in the virtual digital twin environment before deploying the robot to perform actual physical tasks. By pre-training the robot's manipulation skills in simulation, the system reduces the need for time-consuming and potentially dangerous real-world trial-and-error interactions.
2Adaptability or versatility
If current reconstruction or modeling approaches are used, then a digital model of the environment can be created, but the approaches lack efficiency and cannot handle multi-step operations
Solution Approach 1:
The patent implements a dynamic task planning system that can adapt to different task requirements and generate appropriate motion sequences on-the-fly. Rather than using static, pre-programmed approaches, the system dynamically creates and adjusts motion plans based on the specific task at hand, enabling both versatility in handling different operations and efficiency through optimized planning.
Solution Approach 2:
The patent segments complex tasks into multiple discrete motion steps or operations. By breaking down multi-step tasks into individual actionable segments, the system can efficiently plan and execute each step while maintaining the ability to handle complex sequences, thereby improving both versatility and productivity.
3Speed
If single-step grasping and manipulation operations are performed, then the operation can be completed quickly, but complex tasks requiring multiple steps cannot be effectively executed
Solution Approach 1:
The patent divides complex manipulation tasks into multiple discrete motion steps or operations. Each step can be executed efficiently, while the sequence of steps enables handling of complex tasks. This segmentation allows the system to maintain speed at the individual operation level while achieving versatility through multi-step sequences.
Solution Approach 2:
The patent implements continuous task execution by seamlessly connecting multiple motion steps into a coherent sequence. Rather than interrupting between individual operations, the system maintains continuous useful action throughout the multi-step task, improving overall efficiency while handling complex operations.
Data Source
AI summary
It is recognized herein that current approaches to autonomous operations are often limited to grasping and manipulation operations that can be performed in a single step. It is further recognized herein that there are various operations in robotics (e.g., assembly tasks) that require multiple steps or a sequence of motions to be performed. To determine or plan a sequence of motions for fulfilling a task, an autonomous system that includes a robot can perform object recognition, pose estimation, affordance analysis, decision-making, probabilistic task or motion planning, and object manipulation.