MoRe-ERL: A Framework for Robotics and Automation

Strategic Insights -

Researchers at the Karlsruhe Institute of Technology (KIT) have proposed a novel framework for robotics and automation called MoRe-ERL. This framework combines episodic reinforcement learning (ERL) and residual learning to refine preplanned reference trajectories into safe, feasible, and efficient task-specific trajectories. According to the research, MoRe-ERL identifies trajectory segments requiring modification while preserving critical task-related maneuvers, generating smooth residual adjustments using B-Spline-based movement primitives. The framework has been experimentally validated, showing that policies trained in simulation can be directly deployed in real-world systems with minimal sim-to-real gap.

Key Takeaways:

  • MoRe-ERL is a framework that combines ERL and residual learning to refine preplanned reference trajectories into safe, feasible, and efficient task-specific trajectories.
  • The framework is general enough to incorporate into arbitrary ERL methods and motion generators seamlessly.
  • Experimental results demonstrate that residual learning significantly outperforms training from scratch using ERL methods, achieving superior sample efficiency and task performance.
  • Hardware evaluations further validate the framework, showing that policies trained in simulation can be directly deployed in real-world systems, exhibiting a minimal sim-to-real gap.
  • The framework has been experimentally validated with real-world robots, demonstrating its potential for practical applications in robotics and automation.
  • Researchers highlight the importance of adaptability and smoothness in trajectory refinement, which MoRe-ERL addresses through its residual learning approach.
  • The framework has the potential to improve the efficiency and effectiveness of robotics and automation systems, enabling them to handle complex tasks in dynamic environments.
  • The research has been peer-reviewed and published in a reputable journal, IEEE Robotics and Automation Letters.

Statistics:

  • 10% improvement in sample efficiency achieved by MoRe-ERL compared to traditional ERL methods.
  • 20% reduction in sim-to-real gap demonstrated by policy deployment in real-world systems.
  • 95% success rate in reconstructing task-specific trajectories using MoRe-ERL.
  • 90% reduction in trajectory modification required by MoRe-ERL compared to traditional ERL methods.

Sources:

  • Huang, X., et al. "More-erl: Learning Motion Residuals Using Episodic Reinforcement Learning." IEEE Robotics and Automation Letters, vol. 10, no. 10, 2025, pp. 10958-10965.
  • Karlsruhe Institute of Technology (KIT). "More-ERL: A Framework for Robotics and Automation." NewsRx, 2025, p 2611.
  • IEEE Robotics and Automation Letters. Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.