Robot Control Parameter Interpolation for Real-Time Sensor Response
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Solution Overview
Problem
Existing robotics control systems face challenges in integrating sensor feedback and sophisticated control logic due to real-time constraints, making it difficult to perform complex actions and react to external stimuli effectively.
Innovation Solution
A real-time bridge system that translates non-real-time control commands into interpolated control parameters, allowing robots to incorporate sensor feedback and adjust control parameters in real-time, enabling more precise and reliable movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If open-loop control is used to specify robot actions precisely in advance, then manufacturing precision is improved, but the ability to integrate sensor feedback and react to external stimuli deteriorates
Solution Approach 1:
The system performs preliminary computation of robot actions using reinforcement learning before real-time execution. The pre-computed actions serve as a foundation that can be quickly adjusted during execution based on sensor feedback, allowing the system to maintain precision while gaining adaptability.
Solution Approach 2:
A real-time controller acts as an intermediary between the pre-computed actions and the robot execution. This intermediary can rapidly adjust the pre-computed actions based on sensor feedback during execution, bridging the gap between open-loop precision and closed-loop adaptability.
2Reliability
If tight timing constraints are imposed on real-time robot control, then reliability is improved, but the compute time available for sophisticated control logic deteriorates
Solution Approach 1:
Complex control computations are performed in advance during a planning phase, generating pre-computed actions that can be executed in real-time with minimal computation. This separates the heavy computational burden from the tight real-time execution window.
Solution Approach 2:
The control system is divided into two segments: an offline planning segment that performs sophisticated computations using reinforcement learning, and an online execution segment that implements pre-computed actions with minimal real-time computation. This segmentation allows complex logic to be developed without compromising real-time performance.
3Adaptability or versatility
If non-real-time control commands are used to allow sophisticated control logic, then adaptability is improved, but the real-time response capability deteriorates
Solution Approach 1:
Sophisticated control logic generates pre-computed actions in advance, which are then ready for rapid real-time execution. The adaptive decision-making is performed beforehand, allowing the execution phase to respond quickly to changing conditions.
Solution Approach 2:
A real-time controller serves as an intermediary that can rapidly adjust pre-computed actions based on sensor feedback during execution. This intermediary enables the system to maintain both sophisticated control logic and fast real-time response.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing interpolated robot control parameters. One of the methods includes receiving, by a real-time bridge from a control agent for a robot, a non-real-time command for the robot, wherein the non-real-time command specifies a trajectory to be attained by a component of the robot and a target value for a control parameter, wherein the control parameter controls how a real-time controller will cause the robot to react to one or more external stimuli encountered during a control cycle of the real-time controller. The real-time bridge provides the one or more real-time commands translated from the non-real-time command and interpolated control parameter information to the real-time controller, thereby causing the robot to effectuate the trajectory of the non-real-time command according to the interpolated control parameter information.