Autonomous Agent Curiosity Module for Exploration Planning

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Solution Overview

Problem

Agents deployed in uncertain environments face challenges in planning and exploration, as they are typically goal-oriented and lack the ability to efficiently learn about their surroundings, leading to suboptimal performance in achieving goals.

Innovation Solution

A system comprising a goal state module, a curiosity module, and a planning module that calculates a goal state, determines an uncertainty value, and computes a curiosity reward to balance goal-seeking behavior with exploration, enabling agents to update their motion plans and improve environmental understanding.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If agents are goal-oriented and focused on achieving specific goals, then task completion efficiency is improved, but exploration capability and environmental understanding deteriorate

Engineering Contradiction:

Improvetask completion efficiencyVSAvoidexploration capability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent combines goal-oriented behavior and curiosity-driven exploration into a unified decision-making framework. The planning module integrates both the goal state (task completion) and curiosity reward (environmental understanding) to generate motion plans that simultaneously advance both objectives, resolving the contradiction between productivity and adaptability

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system dynamically adjusts the weighting between goal achievement and curiosity exploration by modifying the curiosity reward parameter. When uncertainty is high, the curiosity reward increases, shifting behavior toward exploration; when uncertainty is low, goal-oriented behavior is prioritized, thus adaptively balancing productivity and exploration capability

Inventive Principle:

Principle #35Parameter changes

2Reliability

If agents prioritize goal-seeking behavior, then task achievement is improved, but environmental learning and understanding deteriorate

Engineering Contradiction:

Improvetask achievementVSAvoidenvironmental learning

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system implements a feedback mechanism where the curiosity module continuously monitors environmental uncertainty and adjusts the curiosity reward accordingly. This feedback loop ensures that agents learn about their environment precisely when needed (high uncertainty), while maintaining reliable task achievement when environmental understanding is sufficient (low uncertainty)

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The agent performs preliminary exploration actions when environmental uncertainty is detected, gathering information about the environment before attempting complex goal-oriented tasks. This preliminary action reduces information loss by ensuring the agent has adequate environmental understanding before relying heavily on goal-seeking behavior

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If agents increase exploration behavior to learn about the environment, then environmental understanding is improved, but task completion efficiency deteriorates

Engineering Contradiction:

Improveenvironmental understandingVSAvoidtask completion efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system applies partial exploration action rather than full exploration. The curiosity reward is calculated as a portion of the total reward signal, allowing agents to perform some exploration (partial action) while still maintaining focus on goal completion. This prevents excessive exploration that would completely undermine task efficiency while still gaining environmental understanding

Inventive Principle:

Principle #16Partial or excessive action

4Device complexity

If agents operate in uncertain environments without curiosity-driven exploration, then system simplicity is maintained, but planning ability and performance deteriorate

Engineering Contradiction:

Improvesystem simplicityVSAvoidplanning ability

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The system segments the planning process into distinct modular components: a goal module that handles task completion, a curiosity module that handles environmental exploration, and a planning module that integrates both. This segmentation maintains relative system simplicity through clear module boundaries while improving planning ability by allowing each module to specialize in its function

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11708089B2Systems and methods for curiosity development in agents

Publication Date: 2023.07.25 HONDA MOTOR CO LTD

AI summary

Systems and methods for curiosity development in an agent located in an uncertain environment are provided. In one embodiment, the system includes a goal state module, a curiosity module, and a planning module. The goal module is configured to calculate a goal state of a goal associated with the environment. The curiosity module is configured to determine an uncertainty value for the environment and calculate a curiosity reward based on the uncertainty value. The planning module is configured to update a motion plan based on the goal state and the curiosity reward.