Robot Control Training With TAMP, Imitation, and RL for Long-Horizon Tasks
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Solution Overview
Problem
Conventional robot control techniques face challenges in designing rewards for multi-step tasks and require extensive demonstration data, limiting their adaptability and scalability, especially in long-horizon robotic tasks.
Innovation Solution
A synergistic framework combining task-and-motion planning (TAMP) with behavior cloning and reinforcement learning, using sparse success-based rewards and selective training to handle routine skills with TAMP and fine-tune challenging skills with RL, reducing the need for large demonstration datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is used for robot control in multi-step tasks, then the robot can learn complex skills, but the reward design becomes challenging and requires careful tuning for each stage
Solution Approach 1:
The patent segments the long-horizon task into multiple subtasks with intermediate goals. Each subtask has its own simplified reward signal based on achieving that specific intermediate goal, rather than requiring a single complex reward function for the entire task sequence. This segmentation makes reward design more manageable and less challenging.
Solution Approach 2:
The patent introduces task-and-motion planning (TAMP) as an intermediary component that generates intermediate goals and subtasks. This intermediary breaks down the complex task into manageable stages, allowing the reinforcement learning agent to receive simpler, more direct reward signals for achieving each subtask rather than navigating the entire complex task space directly.
2Reliability
If behavior cloning is used to train robot control policy through demonstrations, then the robot can replicate demonstrated actions, but extensive and high-quality demonstration data is required
Solution Approach 1:
The patent segments the demonstration data requirement by focusing behavior cloning on specific subtasks rather than requiring complete demonstration sequences for entire long-horizon tasks. By breaking down tasks into subtasks, the system needs demonstrations only for critical portions, reducing the total amount of demonstration data required while maintaining reliability for those key actions.
Solution Approach 2:
The patent uses task-and-motion planning (TAMP) as an intermediary that can handle routine skills without demonstrations. This allows behavior cloning to focus only on challenging subtasks that require demonstrated expertise, reducing the overall demonstration data burden while maintaining high reliability for complex actions through selective application of behavior cloning.
3Productivity
If conventional robot control methods are used for long-horizon tasks, then the system can handle simple subtasks, but adaptability to new or altered subtasks is limited
Solution Approach 1:
The patent creates a dynamic system where task-and-motion planning (TAMP) and reinforcement learning work together in a flexible manner. TAMP handles routine subtasks efficiently, while RL provides adaptability for novel or altered subtasks. The system can dynamically switch between these approaches based on the specific subtask requirements, enabling both high productivity for simple tasks and adaptability for new tasks.
Solution Approach 2:
The patent creates a universal robot control system that integrates multiple approaches (TAMP for planning and routine tasks, RL for complex and novel tasks, and behavior cloning for demonstrated skills). This multi-functional system can adapt to different types of subtasks within the same long-horizon task, maintaining productivity for routine operations while gaining versatility for new challenges.
Data Source
AI summary
The disclosed method for training one or more robot control models includes performing, based on one or more demonstration trajectories of a robot performing one or more skills associated with a task, one or more training operations to generate one or more first trained machine learning models for controlling the robot; and performing one or more reinforcement learning operations using the one or more first trained machine learning models to generate one or more second trained machine learning models for controlling the robot.