arxiv_robotics 95% Match Research Paper Robotics Researchers,ML Engineers,Control Engineers 3 weeks ago

Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies

reinforcement-learning › robotics-rl

📄 Abstract

Abstract: This work focuses on sampling strategies of configuration variations for generating robust universal locomotion policies for quadrupedal robots. We investigate the effects of sampling physical robot parameters and joint proportional-derivative gains to enable training a single reinforcement learning policy that generalizes to multiple parameter configurations. Three fundamental joint gain sampling strategies are compared: parameter sampling with (1) linear and polynomial function mappings of mass-to-gains, (2) performance-based adaptive filtering, and (3) uniform random sampling. We improve the robustness of the policy by biasing the configurations using nominal priors and reference models. All training was conducted on RaiSim, tested in simulation on a range of diverse quadrupeds, and zero-shot deployed onto hardware using the ANYmal quadruped robot. Compared to multiple baseline implementations, our results demonstrate the need for significant joint controller gains randomization for robust closing of the sim-to-real gap.

Key Contributions

This paper introduces novel sampling strategies for configuration variations to generate robust universal locomotion policies for quadrupedal robots. By investigating the effects of sampling physical robot parameters and joint proportional-derivative gains, it enables training a single reinforcement learning policy that generalizes to multiple parameter configurations, improving robustness and enabling zero-shot deployment.

Business Value

Enables the development of more adaptable and reliable legged robots for diverse environments and tasks, reducing the need for extensive re-training for each new configuration.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, demonstrated by zero-shot deployment onto hardware.

Limitations Addressed

Lack of robustness and generalization in locomotion policies across different robot configurations and parameters.

Performance Gains

Compared to multiple baseline implementations, results demonstrate the need for significant joint controller gains randomization.

Technical Tags

reinforcement learninglocomotion policiesquadrupedal robotsparameter samplingjoint gainsrobustnessgeneralizationadaptive filteringzero-shot deployment

Research Topics

RoboticsReinforcement LearningControl SystemsRobot LocomotionPolicy Generalization

Methods & Architectures

Reinforcement LearningParameter SamplingAdaptive FilteringZero-shot Deployment Policy Network

Applications & Tasks

Robotics Autonomous Systems Robustness in LocomotionGeneralization across ConfigurationsPolicy Training Efficiency Quadrupedal LocomotionPolicy Generalization

Related Fields

RoboticsMachine LearningControl TheoryArtificial Intelligence

Keywords

quadrupedal locomotionreinforcement learningsampling strategiesrobustnessgeneralizationparameter tuningjoint gainsadaptive filteringzero-shotrobot controlpolicy learningsim-to-real

Academic Context

#Robotics#Reinforcement Learning#Control Systems#Robot Locomotion#Policy Generalization

Technology Stack

Frameworks & Libraries

RaiSim

Commercial Potential

Potential Products

General-purpose legged robot control softwareRobotics simulation platforms

Target Industries

RoboticsManufacturingLogisticsExploration

Use Case Examples

Autonomous navigation for quadrupedal robots in varied terrainsRobots adapting to different physical properties

Competitive Edge

Improves upon existing RL-based locomotion methods by enhancing robustness and generalization through intelligent sampling strategies.

Market Opportunity

Growing market for legged robots.

Revenue Models

Licensing of control algorithmsspecialized robot hardware.

Resource Requirements

Compute Needs

Significant compute for RL training.

Data Requirements

Simulated robot configurations and parameters.

Deployment Constraints

Requires accurate robot models and simulation environments for training.

Scalability

Scalable to different quadrupedal robot designs and locomotion tasks.

Production Readiness

Maturity Level

Research

Time to Market

2-5 years

Patent Potential

Low

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