Imitation-relaxation reinforcement learning for sparse badminton strikes via dynamic trajectory generation.

ABSTRACT: Robotic racket sports provide exceptional benchmarks for evaluating dynamic motion control capabilities in robots. Due to the highly non-linear dynamics of the shuttlecock, the stringent demands on robots' dynamic responses, and the convergence difficulties caused by sparse rewards in reinforcement learning, badminton strikes remain a formidable challenge for robot systems. To address these issues, this study proposes DTG-IRRL, a novel learning framework for badminton strikes that integrates imitation-relaxation reinforcement learning with dynamic trajectory generation. The framework demonstrates significantly improved training efficiency and performance, achieving faster convergence and twice the landing accuracy. Analysis of the reward function within a specific parameter space hyperplan

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC12436432 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature