Robot Damage Recovery Using Multi-Objective Behavior Maps

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Solution Overview

Problem

Conventional single-objective reinforcement learning methods for robot damage recovery fail to fully explore the solution space and often get stuck in local optima, limiting the discovery of better global solutions, especially in multi-legged robots that need to adapt to damaged environments.

Innovation Solution

A method for robot damage recovery based on multi-objective MAP-Elites, involving a behavior map construction phase and a damage adaptation phase, which utilizes a multi-dimensional behavior map and a Gaussian process model to simulate undamaged and damaged environments, decomposing fitness functions into distance and cost objectives, and employing adaptive weight adjustments to optimize controller parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If single-objective reinforcement learning is used for robot damage recovery, then the training process is simpler, but the algorithm gets stuck in local optima and fails to explore the full solution space

Engineering Contradiction:

Improvetraining process complexityVSAvoidsolution space exploration capability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent segments the single objective function into multiple objective functions (distance fitness and cost fitness). Instead of optimizing one combined objective, the algorithm separately evaluates and optimizes multiple objectives simultaneously, allowing the robot to explore diverse solutions that balance different performance criteria during damage recovery

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent adds dimensionality to the optimization problem by introducing multiple objectives and using a behavior map with multi-dimensional grids. Each grid cell represents a different region of the solution space defined by behavior characteristics, enabling exploration across multiple dimensions rather than along a single objective gradient

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Device complexity

If multiple potential performance indexes are combined into a single objective function, then the optimization process is simplified, but valuable information about interdependencies among objectives is lost

Engineering Contradiction:

Improveoptimization process complexityVSAvoidinformation about objective interdependencies

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent segments the performance evaluation into distinct objective functions (distance fitness evaluating forward movement capability and cost fitness evaluating energy consumption). Each objective is evaluated separately and stored in the behavior map, preserving the unique information and characteristics of each performance metric without losing interdependency relationships

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements feedback mechanisms where evaluation results from multiple objectives are used to update the behavior map and guide subsequent search. The algorithm uses gradient information from each objective to inform the evolutionary process, allowing the system to learn and adapt based on performance feedback across all objectives simultaneously

Inventive Principle:

Principle #23Feedback

3Use of energy by moving object

If single-objective optimization is used, then computational resources are saved, but the breadth of search space exploration is limited

Engineering Contradiction:

Improvecomputational resource consumptionVSAvoidsearch space exploration breadth

Core Design Contradiction:

Use of energy by moving objectVSAdaptability or versatility

Solution Approach 1:

The patent performs preliminary action by pre-training the robot in an undamaged environment to build a comprehensive behavior map before damage occurs. This pre-computed map stores diverse controller parameters and their performance characteristics, enabling rapid adaptation to damage without requiring extensive real-time computational resources during the actual damage recovery scenario

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a behavioral copy or map of the solution space from undamaged environment training. This behavior map serves as a repository of pre-explored solutions that can be queried and adapted for damaged scenarios, avoiding the need to re-explore the entire search space when damage occurs

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250353557A1Method for robot damage recovery based on multi-objective map-elites

Publication Date: 2025.11.20 SOUTH CHINA UNIV OF TECH

AI summary

Provided is a method for robot damage recovery based on multi-objective MAP-Elites, relating to the technical field of robot control. The method includes initializing a behavior map, and picking one parent controller parameter from the behavior map; employing a plurality of sample controller parameters to guide the direction of improvement, through gradient-based updates derived from performance feedback, and evolving the parental controller parameter into a child controller parameter; based on a dominance relationship, updating the parameters within the grids of the behavior map; initializing a damage recovery model using a map-based Bayesian optimization algorithm and the behavior map; adjusting and searching the damage recovery model to obtain an optimal controller parameter. Compared to existing technologies, this method enables the acquisition of controller parameters that enable robot damage recovery in a damaged environment without the need for interaction with a real environment, significantly reducing search time and effectively enhancing computational efficiency.