Enterprise AI Analysis
CROSS: A Continual Robotic Simulation Suite for Scalable Reinforcement Learning with High Task Diversity and Realistic Physics Simulation
This paper introduces CROSS, a novel benchmark suite for Continual Reinforcement Learning (CRL) featuring realistically simulated robots in Gazebo. It offers high task diversity and physics simulation, addressing key limitations of existing benchmarks like Continual World. CROSS supports two robotic platforms—a differential-drive robot for line-following and object-pushing, and a 7-d.o.f. robotic arm for goal-reaching—with multiple control modalities. The suite is designed for reproducibility, scalability, and easy extensibility, providing a containerized setup and baseline evaluations of standard RL algorithms.
Executive Impact Summary
CROSS delivers significant advancements for enterprise AI development in robotics by providing a robust, scalable, and realistic simulation environment. Its high task diversity enables comprehensive testing of continual learning algorithms, reducing development cycles for adapting AI to new scenarios. The inclusion of kinematic variants allows for rapid iteration and hyper-parameter tuning, accelerating research by orders of magnitude. Furthermore, its ROS compatibility ensures direct transferability of trained agents from simulation to real-world hardware, significantly de-risking deployment and fostering true sim-to-real transfer for continuous adaptation.
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Faster Kinematic Simulations
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Distinct Tasks for Diversity
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Simulation-to-Real Fidelity
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Deployment Risk
Deep Analysis & Enterprise Applications
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CROSS integrates Gazebo for physics-based simulation with a standardized communication framework, enabling high-fidelity robotic experiments. It supports modular extensibility and is compatible with ROS for seamless sim-to-real deployment.
Enterprise Process Flow
Gazebo Simulator (Physics)
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Robotic Platforms (2-wheel, 7-DOF Arm)
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Sensor Integration (Lidar, Camera, Bumper)
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Task Generation (Visual/Structural Params)
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RL Agent (DQN, REINFORCE)
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Continual Learning (Policy Update)
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Performance Evaluation (Forgetting, Transfer)
Experiments reveal that standard RL algorithms exhibit catastrophic forgetting when trained sequentially on CROSS tasks. Kinematic variants offer significant speed-up for hyperparameter tuning without sacrificing learning dynamics, while full Gazebo simulations confirm realism.
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The containerized (Apptainer) setup ensures out-of-the-box reproducibility across diverse Linux systems, dramatically simplifying installation and setup for researchers. This fosters wider adoption and validation of CRL algorithms.
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Reproducibility Score
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Enterprise AI Roadmap
A structured approach to integrating continual learning capabilities, tailored for your specific business needs.
Phase 01: Discovery & Strategy
Conduct a deep dive into existing workflows, identify key areas for CRL application, and define success metrics. Develop a strategic roadmap aligned with business objectives.
Phase 02: Pilot & Proof-of-Concept
Implement a small-scale pilot project using CROSS to validate the chosen CRL approach, demonstrate feasibility, and gather initial performance data within a controlled environment.
Phase 03: Scaled Deployment & Integration
Expand the solution across relevant enterprise systems, integrating with existing infrastructure. Establish monitoring and feedback loops for continuous improvement.
Phase 04: Continuous Optimization & Learning
Leverage the CRL framework to adapt to evolving conditions, new tasks, and dynamic environments, ensuring long-term system resilience and optimal performance.
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