Robot Self-Region Control for Assertive Human Cooperation
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Solution Overview
Problem
Conventional robots primarily perform passive actions and lack the capability to execute assertive actions due to the inability to consider the self-region of a person and the intention of the robot in cooperative tasks.
Innovation Solution
A control device that estimates a self-region of a partner based on preference information derived from observation sensor data and a free energy principle, determines the self-region of the robot in consideration of both the partner's self-region and the robot's intention, and generates action information to control the robot's operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the robot performs only passive actions to meet human expectations, then human-robot cooperation is smooth, but the robot cannot perform assertive actions to achieve its own goals
Solution Approach 1:
The patent applies dynamics by making the robot's behavioral mode flexible and adjustable. The control device dynamically switches between passive action modes (following human intentions) and assertive action modes (achieving robot's own goals) based on the determined self-region and current situation. This allows the robot to adapt its level of assertiveness dynamically rather than being fixed in one mode.
Solution Approach 2:
The patent applies local quality by defining different interaction qualities in different spatial regions. The self-region determination creates zones around the robot where different levels of assertiveness apply - in the self-region, the robot performs assertive actions to maintain its goals, while outside this region, it performs passive actions to accommodate human intentions. This spatial differentiation resolves the contradiction by allowing both behaviors in appropriate contexts.
2Reliability
If the robot estimates only the degree of recognition from the human, then interference avoidance is achieved, but the self-region of the person and intention of the robot are not considered
Solution Approach 1:
The patent applies segmentation by dividing the spatial environment into distinct regions relative to the robot - specifically the self-region and non-self-region. This segmentation allows the control device to apply different control strategies in different regions: in the self-region, the robot prioritizes its own intentions and performs assertive actions, while in non-self-regions, it prioritizes human expectations and performs passive actions. This resolves the contradiction by structuring the decision-making space.
Solution Approach 2:
The patent introduces the self-region concept as an intermediary between the robot's sensor data and its action selection. Rather than directly mapping recognition degree to avoidance behavior, the self-region acts as a mediating construct that integrates both human expectations and robot intentions. This intermediary enables more nuanced and appropriate assertive actions while maintaining reliability in interference avoidance.
3Adaptability or versatility
If the robot determines self-region based on both partner's expectations and robot's intention, then assertive actions become possible, but the control complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-determining the self-region based on the robot's physical characteristics, sensor capabilities, and typical operational parameters before actual interaction occurs. This self-region is established as a baseline that incorporates the robot's intentions and goals. During interaction, the control device only needs to compare current human expectations against this pre-established self-region, rather than computing everything in real-time. This reduces control complexity while maintaining assertive action capability.
Data Source
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Figure 3A~3B
AI summary
There is provided a control device including: a self-region estimation unit configured to estimate a self-region of a partner based on preference information indicating an observation that the partner is estimated to expect for a control target and the partner, the preference information being derived based on a predetermined principle and observation sensor data acquired from the partner and the control target, the partner being a person or an autonomous system, and the control target being an autonomous system; a self-region determination unit configured to determine a self-region of the control target based on preference information indicating an observation that the control target expects for the partner and the control target, which is derived based on the predetermined principle, using the self-region of the partner and an intention of the control target; and an action generation unit configured to generate action information for controlling an action of the control target based on the self-region of the control target, thereby controlling an operation of the control target.