Goal-Conditioned Robot Policy Learning Across Viewpoint Mismatch
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for controlling robots using neural networks struggle with complex tasks that require contact-rich interactions with multiple objects, such as stacking or manipulating objects, especially when demonstrations are captured from a third-person view and the robot's observations are from a first-person perspective.
Innovation Solution
A system that trains a policy neural network using imitation learning, allowing it to select actions based on embeddings of current and goal observations from different perspectives, enabling successful performance of complex tasks by conditioning the policy neural network on both current and goal states, even when demonstrations and observations are from different viewpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing third-person imitation methods are used, then simpler tasks like reaching and lifting can be handled, but performance on complex tasks requiring contact-rich longer sequences of object interactions deteriorates
Solution Approach 1:
The patent introduces an embedding neural network as an intermediary that transforms third-person demonstration observations into a unified embedding space. This mediator enables the policy neural network to effectively bridge the gap between third-person demonstrations and first-person execution, resolving the contradiction between handling complex tasks and maintaining execution reliability
Solution Approach 2:
The patent replaces traditional imitation learning mechanisms with a neural network-based embedding system. By substituting conventional approaches with deep learning embeddings, the system achieves reliable performance on complex contact-rich tasks that previous methods could not handle
2Ease of manufacture
If demonstrations are captured from third-person view, then training data is easily obtained, but the robot's first-person observations create a domain mismatch that deteriorates policy learning effectiveness
Solution Approach 1:
The embedding neural network serves as a mediator that translates third-person observations into a perspective-invariant embedding space. This intermediary representation preserves essential task information while eliminating perspective-specific details, enabling effective policy learning from third-person demonstrations for first-person execution
Solution Approach 2:
The patent transforms observations from different perspectives by changing their representation parameters through neural network embeddings. By mapping third-person and first-person observations into a unified embedding space, the system maintains information consistency across different viewpoints
3Ease of operation
If policy neural network is trained on third-person demonstrations, then training is simplified, but applying the policy to first-person environment deteriorates performance on complex tasks
Solution Approach 1:
The embedding neural network acts as a bridge between third-person demonstrations and first-person execution. It processes third-person observations into embeddings that the policy neural network can use effectively, while the embeddings capture task-relevant information that transfers well to first-person perspectives, resolving the performance deterioration issue
Data Source
AI summary
It is described a system implemented as computer programs on one or more computers in one or more locations that trains a policy neural network that is used to control a robot, i.e., to select actions to be performed by the robot while the robot is interacting with an environment, through imitation learning in order to cause the robot to perform particular tasks in the environment.