痙縮条件下における外骨格の強化学習による適応トルク制御
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning
脳性麻痺や脊髄損傷などによる痙縮を持つ人向けに、深層強化学習を用いて膝外骨格の適応トルク制御器を開発し、シミュレーションで従来のコンプライアント制御より最大トルクと振動を低減できることを示した。
著者: Andrés Chavarrías, David Rodriguez-Cianca, Pablo Lanillos
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, being one of the most disabling features in the progression of these diseases. Despite the potential benefit of using wearable robots to treat spasticity, their use is not currently recommended to subjects with a level of spasticity above ${1^+}$ on the Modified Ashworth Scale. The varying dynamics of this velocity-dependent tonic stretch reflex make it difficult to deploy safe personalized controllers. Here, we describe a novel adaptive torque controller via deep reinforcement learning (RL) for a knee exoskeleton under joint spasticity conditions, which accounts for task performance and interaction forces reduction. To train the RL agent, we developed a digital twin, including a musculoskeletal-exoskeleton system with joint misalignment and a differentiable spastic reflexes model for the muscles activation. Results for a simulated knee extension movement showed that the agent learns to control the exoskeleton for individuals with different levels of spasticity. The proposed controller was able to reduce maximum torques applied to the human joint under spastic conditions by an average of 10.6\% and decreases the root mean square until the settling time by 8.9\% compared to a conventional compliant controller.
