Asynchronous Robotic Action Control for Low-Latency Motion
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Solution Overview
Problem
Existing reinforcement learning techniques for robotic control, such as QT-Opt, often operate in a blocking manner, where the next best action is determined only after the completion of the previous action, leading to latency issues in real-world robotic tasks, causing wear and tear, delayed task completion, and suboptimal reactions to environmental changes.
Innovation Solution
Asynchronous robotic control using a trained critic network that determines the next action before the previous action is completed, incorporating most recently selected robotic action data to process candidate actions and select the best action based on current and target states, allowing for simultaneous implementation of actions and improved reaction times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot waits for completion of the previous action before determining the next action, then the control system can ensure sequential execution, but the system experiences increased latency and delayed reaction to environmental changes
Solution Approach 1:
The system performs preliminary action by starting to determine the next robotic action before the previous action is completed. The critic network begins processing candidate actions for the next step while the robot is still executing the current action, allowing overlap between action determination and execution phases.
Solution Approach 2:
The control system transitions from a static sequential blocking mode to a dynamic asynchronous mode where action determination and execution can occur in parallel. The system dynamically adjusts by using the most recently selected robotic action data to inform next action selection, enabling adaptive overlap of control cycles.
2Measurement precision
If the robot decelerates to zero velocity while determining the next action, then the system can compute actions more accurately, but this causes wear and tear on robotic components
Solution Approach 1:
The system performs preliminary computation of the next action while the robot is still moving at operational velocity. The critic network processes candidate actions in advance during the execution of the current action, eliminating the need to decelerate to zero for computation.
Solution Approach 2:
The robot maintains continuous motion without unnecessary deceleration to zero velocity. By overlapping action determination with action execution, the system keeps the robot in a state of useful motion throughout, avoiding idle stopping that causes mechanical wear.
3Ease of operation
If the robot decelerates to zero velocity during action determination, then the system can process actions sequentially, but this results in delayed reaction to dynamic environmental changes
Solution Approach 1:
The system performs preliminary determination of the next action before the current action completes, using the critic network to evaluate candidate actions in advance. This allows the robot to react more quickly to environmental changes by having the next action already prepared.
Solution Approach 2:
The control system dynamically adapts by continuously determining actions in parallel with execution, rather than following a rigid sequential pattern. The system uses the most recently selected robotic action data to maintain adaptability while processing actions efficiently.
4Device complexity
If the system uses blocking control where next action is determined after previous action completion, then the control flow is simple, but the overall task completion time increases due to cumulative latencies
Solution Approach 1:
The system performs preliminary determination of subsequent actions while previous actions are still being executed. The critic network evaluates candidate actions in advance, creating a pipeline where action determination for step N+1 occurs during execution of step N, reducing overall task completion time.
Solution Approach 2:
The control system maintains continuous productive activity by overlapping action determination with action execution. Instead of having idle periods where the robot stops to compute the next action, the system keeps both computation and execution phases continuously active in parallel.
Data Source
AI summary
Asynchronous robotic control utilizing a trained critic network. During performance of a robotic task based on a sequence of robotic actions determined utilizing the critic network, a corresponding next robotic action of the sequence is determined while a corresponding previous robotic action of the sequence is still being implemented. Optionally, the next robotic action can be fully determined and/or can begin to be implemented before implementation of the previous robotic action is completed. In determining the next robotic action, most recently selected robotic action data is processed using the critic network, where such data conveys information about the previous robotic action that is still being implemented. Some implementations additionally or alternatively relate to determining when to implement a robotic action that is determined in an asynchronous manner.