The field of soft robotics is experiencing significant growth, driven by advancements in reinforcement learning and simulation techniques. Researchers are developing innovative methods to model and control soft robots, overcoming challenges such as nonlinear and hysteretic behavior. A key trend is the use of reinforcement learning to optimize control policies, allowing for precise and adaptive control of soft robots in complex environments. Noteworthy papers include one that proposed a hysteresis-aware whole-body neural network model, achieving a 84.95% reduction in Mean Squared Error compared to traditional modeling methods. Another notable paper introduced a reinforcement learning-based framework for visual servoing tasks on soft continuum arms, demonstrating a 99.8% success rate in simulation and 67% in zero-shot sim-to-real transfer.
Soft Robotics and Reinforcement Learning Advancements
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Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots
Performance Analysis of a Mass-Spring-Damper Deformable Linear Object Model in Robotic Simulation Frameworks
Autonomous Control of Redundant Hydraulic Manipulator Using Reinforcement Learning with Action Feedback
CaRoSaC: A Reinforcement Learning-Based Kinematic Control of Cable-Driven Parallel Robots by Addressing Cable Sag through Simulation
Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms
MAT-DiSMech: A Discrete Differential Geometry-based Computational Tool for Simulation of Rods, Shells, and Soft Robots
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning