LLM-Based Robot Skill Learning with Uncertainty Inference
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Solution Overview
Problem
Conventional robot manipulation skill learning requires significant human involvement and supervision, leading to inefficiencies and potential safety issues due to fatigue and judgment errors in real-world demonstrations.
Innovation Solution
An automated method using a large language model to enable robots to learn and improve natural-language-based manipulation skills with minimal human intervention, by generating subtasks, inferring task and cognitive uncertainties, and adjusting performance weights based on skill uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world demonstration data is collected to improve robot manipulation skills, then learning effectiveness is improved, but human involvement and cost increase significantly
Solution Approach 1:
The robot performs self-demonstration by autonomously executing tasks and generating its own demonstration data without human intervention. The system captures the robot's own action sequences during task execution and uses this self-generated data for skill learning, eliminating the need for human demonstrators while maintaining learning effectiveness.
Solution Approach 2:
The system generates synthetic demonstration data by copying and simulating task execution patterns. Instead of requiring actual human demonstrations, the robot creates virtual demonstration sequences through simulation environments that replicate real-world task dynamics, providing sufficient training data without human involvement.
2Manufacturing precision
If continuous human supervision is provided during demonstration, then skill correction is improved, but demonstrator fatigue and efficiency decrease
Solution Approach 1:
The system implements automated feedback mechanisms using reinforcement learning where the robot receives reward signals based on task completion success. The feedback loop continuously evaluates robot performance and adjusts skill parameters automatically, replacing human supervisory correction with algorithmic feedback that maintains accuracy without causing fatigue.
Solution Approach 2:
Human supervisory correction is replaced with automated algorithmic systems. The patent substitutes the mechanical human-demonstrator correction process with computational algorithms that automatically analyze robot performance and generate correction signals, eliminating the need for continuous human supervision while maintaining correction quality.
3Loss of time
If simulation data is used for robot skill learning, then data collection cost and time are reduced, but domain gap with real-world environment persists
Solution Approach 1:
The system dynamically adapts the simulation environment to match real-world conditions by continuously updating simulation parameters based on actual robot performance data. The simulation dynamically adjusts physical properties, object characteristics, and task conditions to reduce the domain gap, allowing fast data collection while improving real-world transferability.
Solution Approach 2:
The system performs preliminary training in simulation environments before deploying to real-world scenarios. By pre-training robot skills in the accelerated simulation domain and then fine-tuning with minimal real-world data, the system achieves both efficient data collection and high real-world applicability, preparing the robot in advance for real-world deployment.
Data Source
AI summary
Disclosed herein is a method for performing a robot skill based on skill uncertainty using a large language model. The method includes generating a subtask list using a large language model by receiving a target task and environment information, mapping a subtask in the subtask list into a skill embedding space through an abstract skill policy network, and performing the subtask by decoding the mapped subtask through a manipulation skill policy network.