Robotic Policy Learning for Contact-Rich Manipulation Control
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Solution Overview
Problem
Existing robotic agents face challenges in learning complex feedback control policies for manipulation tasks with discontinuous contact dynamics, particularly in tasks requiring fine action control, as they struggle to effectively map high-dimensional sensory inputs to motor torques.
Innovation Solution
The implementation of a global policy neural network that trains a two-step approach, first optimizing local policy controllers for various initial conditions and then using these optimized controllers to update the global policy neural network, allowing the robotic agent to learn effective action selection policies from both low-dimensional and high-dimensional observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a global policy neural network is trained to map high-dimensional sensory inputs to motor torques, then the robotic agent can perform complex manipulation tasks with discontinuous contact dynamics, but the training complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the control policy learning into two distinct components: a global policy neural network that processes high-dimensional sensory inputs and selects actions, and local policy controllers that execute fine-grained motor control. This segmentation allows each component to be optimized independently, reducing overall training complexity while maintaining the ability to perform complex manipulation tasks.
Solution Approach 2:
The patent introduces local policy controllers as intermediary components between the global policy neural network and the robotic actuators. These local controllers translate the global policy's action selections into precise motor torques, acting as a mediator that simplifies the training of the global network while ensuring fine action control for contact-rich tasks.
2Measurement precision
If the robotic agent learns from high-dimensional pixel inputs, then the action selection can capture complex visual patterns, but the training time and computational resources required increase
Solution Approach 1:
The patent segments the observation processing into two modes: low-dimensional observations that capture essential state variables for rapid learning, and high-dimensional pixel inputs that provide detailed visual information. The global policy neural network can be trained on low-dimensional inputs for faster convergence, while the ability to process high-dimensional inputs remains available for tasks requiring detailed visual pattern recognition.
Solution Approach 2:
The patent implements a hierarchical approach where the global policy neural network performs partial action selection based on simplified observations, and local policy controllers handle the remaining fine-grained control. This partial action approach at the global level reduces training time while maintaining overall task performance.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing a plurality of instances of the robotic task. For each instance of the robotic task, the method includes generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task in accordance with current values of the parameters of the global policy neural network, and optimizing a local policy controller that is specific to the instance on the trajectory of state-action pairs for the instance. The method further includes generating training data for the global policy neural network using the local policy controllers, and training the global policy neural network on the training data to adjust the current values of the parameters of the global policy neural network.