Analysis of the Application of Reinforcement Learning in Motion Control of Robots in Unstructured Environments

Motion Control Technology for Quadruped Robots Based on Reinforcement Learning

This paper analyzes prevailing challenges and corresponding countermeasures regarding hardware deployment, sample efficiency, and model generalization capacity, and points out that further integration of multi-algorithms, optimization of sim-to-real transformation and overall strategy design will be the main trends in...

Application and evaluation of reinforcement learning for two-dimensional trajectory tracking in snake-like robots

Context—Snake-like robots are biomimetic systems that can move effectively in narrow, complex, and restricted environments thanks to their modular and flexible body structures composed of numerous serially connected joints. These characteristics offer significant advantages, particularly in areas such as pipeline inspe...

A transformer-based reinforcement learning framework for autonomous training of industrial robotic manipulators

Introduction Autonomous robotic manipulation in modern manufacturing requires control policies that simultaneously achieve precision, adaptation, operational safety, and multi-task capability. Methods This paper proposes a Transformer-Based Reinforcement Learning Network (TRL-Net) that combines temporal state encoding,...

Deep Learning Methods for Soft Robot Control: Data-Driven Modeling, Policy Learning, and Dynamic Prediction

. Soft robots, relying on compliant materials, exhibit inherent safety and environmental adaptability in complex environments, but their strong nonlinear characteristics make modeling and control particularly challenging. This paper employs a literature review and comparative analysis approach to summarize research on...

Reinforcement Learning for Quadrupedal Robot Control: Taxonomy, Sim-to-Real, Robustness, and Emerging Trends

A control-oriented review of RL-based quadrupedal robot control, which introduces the main physical and algorithmic foundations, and examines recent research progress from three complementary aspects: control architecture, observation and information design, and training task formulation.

Autonomous intelligent navigation in unknown environments ⋆