Real-World Robot Policy Training With Autonomous Reset and Task Selection
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Solution Overview
Problem
Deep reinforcement learning algorithms face challenges in transferring trained policies from simulated environments to real-world robotic systems, particularly for legged robots, due to discrepancies between simulated and real environments, and require significant human intervention for resetting and safety during training.
Innovation Solution
A system that trains multiple policy networks in a real-world workspace using a multi-task learning procedure and safety-constrained reinforcement learning framework, allowing robots to learn locomotion skills autonomously with minimal human intervention by selecting tasks based on their pose and environment parameters, and incorporating an automated reset mechanism to prevent falls and damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep reinforcement learning is used to train robot policies in simulated environments, then effective control policies can be learned for various robotic applications, but significant performance degradation occurs when transferring to real-world environments due to simulation-reality discrepancy
Solution Approach 1:
The patent uses visual copying by capturing real-world images through cameras and processing them through a visual processing module to generate pixel-based observations. These visual copies of the real environment replace traditional simulated sensor inputs, allowing the robot to learn policies directly from real-world visual data while maintaining the reinforcement learning framework. This resolves the simulation-reality gap by copying real visual information rather than relying on simulated environments.
Solution Approach 2:
The patent introduces a visual processing module as an intermediary between the camera and the reinforcement learning agent. This intermediary processes real-world images to generate appropriate observation states for the policy network, bridging the gap between raw real-world visual data and the structured inputs expected by RL algorithms. This mediator enables effective transfer from real-world visual input to robot control without requiring simulation.
2Extent of automation
If traditional reinforcement learning training is performed in real-world environments, then policies can be trained directly for deployment, but significant human intervention is required for resetting the robot and ensuring safety during training
Solution Approach 1:
The patent implements automated reset functionality where the robot autonomously returns to its initial position and orientation after completing each training episode or falling. The system includes automated state resetting mechanisms that restore the robot's position, orientation, and battery level without human intervention. This self-service capability enables continuous autonomous training by automatically managing the robot's state between episodes.
Solution Approach 2:
The patent incorporates safety constraints and protective mechanisms before training begins, including predefined safe operating zones, maximum duration limits for each episode, and automated monitoring systems. These beforehand protections prevent harmful situations from occurring during training, allowing the robot to learn autonomously without requiring constant human supervision for safety concerns.
3Adaptability or versatility
If multiple policy networks are trained for different locomotion tasks, then the robot can perform diverse locomotion skills, but the training process becomes more complex and requires managing multiple networks
Solution Approach 1:
The patent employs a single universal policy network that can perform multiple locomotion tasks including walking forward, backward, left, right, and rotating. Rather than training separate specialized networks for each direction, the system uses one multi-functional network that learns all locomotion skills through unified reinforcement learning training with task-specific reward functions. This universal approach reduces complexity while maintaining full locomotion versatility.
Solution Approach 2:
The patent implements dynamic task selection where the robot automatically chooses which locomotion task to perform based on its current state, position, and training progress. The training process dynamically adjusts which policies are being learned and how resources are allocated across different locomotion skills. This dynamic approach allows the system to manage multiple locomotion capabilities without requiring static, pre-configured separate networks for each task.
Data Source
AI summary
Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.