RL-ET-DeePC trains a model-free RL policy to trigger DeePC optimizations adaptively, cutting optimization frequency by up to 66% in simulation and 34% on hardware while preserving tracking accuracy for a soft robotic arm.
Velocity-form data-enabled predictive control of soft robots under unknown external payloads,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm
RL-ET-DeePC trains a model-free RL policy to trigger DeePC optimizations adaptively, cutting optimization frequency by up to 66% in simulation and 34% on hardware while preserving tracking accuracy for a soft robotic arm.