Robot Force-Torque Reinforcement Learning for Contact-Rich Tasks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems lack the intelligence to autonomously acquire skills and adapt to complex environments, with limited ability to handle unstructured tasks due to inefficient manual programming and limited feedback control.
Innovation Solution
The implementation of reinforcement learning (RL) methods, specifically guided policy search (GPS) and mirror descent guided policy search (MDGPS), combined with admittance force/torque control and neural networks, to process robot state information and haptic feedback for improved robotic control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual programming is used to control robots, then the robot can perform tasks, but the programming efficiency is low and the system cannot adapt to complex environments
Solution Approach 1:
The robot system performs self-learning through reinforcement learning algorithms, autonomously acquiring skills and adapting to environments without manual programming. The system learns optimal control policies through interaction with the environment, eliminating the need for complex hand-coded programs while improving adaptability to unstructured tasks
Solution Approach 2:
Traditional manual programming mechanisms are replaced with machine learning-based autonomous learning mechanisms. The system uses reinforcement learning algorithms to automatically generate control strategies, substituting the mechanical process of manual code writing with an intelligent learning process that adapts to complex environments
2Reliability
If traditional feedback controllers are used, then the robot can maintain stability, but the control capability for contact-rich tasks is limited
Solution Approach 1:
The system incorporates force/torque sensor feedback from the robot wrist to provide haptic information about contact forces during manipulation tasks. This feedback loop enables the reinforcement learning agent to learn appropriate contact behaviors, combining stability from feedback control with adaptability for contact-rich tasks through autonomous learning of force control policies
Solution Approach 2:
The control system combines traditional feedback control mechanisms with reinforcement learning algorithms to create a hybrid control architecture. This composite approach integrates the stability of classical controllers with the adaptability of machine learning, enabling reliable performance in contact-rich tasks through the synergistic combination of deterministic control and learned behaviors
3Adaptability or versatility
If reinforcement learning with force/torque feedback is implemented, then the robot can perform contact-rich tasks, but the system complexity increases
Solution Approach 1:
The force/torque sensor mounted on the robot wrist serves as an intermediary that provides haptic feedback about contact forces to the reinforcement learning system. This sensor acts as a mediator between the physical contact environment and the learning algorithm, enabling the robot to perceive and adapt to contact forces without requiring complex direct measurement systems
Solution Approach 2:
The system incorporates force and torque parameters from the wrist sensor into the reinforcement learning state space, expanding the observation dimensions. By adding these physical parameters to the learning input, the system enables contact-rich task performance through enriched state representation while managing complexity through efficient neural network processing of the expanded parameter set
Data Source
AI summary
Systems and methods for controlling robots including industrial robots. A method includes executing (402) a program (550) to control a robot (102) by the robot control system (120, 500). The method includes receiving (404) robot state information (554). The method includes receiving (406) force torque feedback (556) inputs from a sensor (554) on the robot (102). The method includes producing (410) a robot control command for the robot (102) based on the robot state information (554) and the force torque feedback (556) inputs. The method includes controlling (412) the robot (102) using the robot control command.