[論文レビュー] See, Learn, Assist: Safe and Self-Paced Robotic Rehabilitation via Video-Based Learning from Demonstration

Ali Alabbas, Camillo Murgia+2 more|arXiv (Cornell University)|Mar 15, 2026

Stroke Rehabilitation and Recovery被引用数 0

ひとこと要約

ビデオを用いた模倣学習フレームワークは、療法士のデモンストレーションを体中心の6-DOF直交座標動作原理（Cartesian Dynamic Movement Primitives）へ encode し、パッシブ・アクティブアシスト・アクティブリジスティブモードにわたって cobot 支援リハビリを安全に適応的に駆動する。GMRベースの力監視と可逆的軌道による即時の安全性確保も行う。

ABSTRACT

In this paper, we propose a novel framework that allows therapists to teach robot-assisted rehabilitation exercises remotely via RGB-D video. Our system encodes demonstrations as 6-DoF body-centric trajectories using Cartesian Dynamic Movement Primitives (DMPs), ensuring accurate posture-independent spatial generalization across diverse patient anatomies. Crucially, we execute these trajectories through a decoupled hybrid control architecture that constructs a spatially compliant virtual tunnel, paired with an effort-based temporal dilation mechanism. This architecture is applied to three distinct rehabilitation modalities: Passive, Active-Assisted, and Active-Resistive, by dynamically linking the exercise's execution phase to the patient's tangential force contribution. To guarantee safety, a Gaussian Mixture Regression (GMR) model is learned on-the-fly from the patient's own limb. This allows the detection of abnormal interaction forces and, if necessary, reverses the trajectory to prevent injury. Experimental validation demonstrates the system's precision, achieving an average trajectory reproduction error of 3.7cm and a range of motion (ROM) error of 5.5 degrees. Furthermore, dynamic interaction trials confirm that the controller successfully enforces effort-based progression while maintaining strict spatial path adherence against human disturbances.

研究の動機と目的

• RGB-Dビデオから療法士によるロボット支援リハビリ練習をリモートで教示可能にする。
• 体中心軌道エンコードを通じて、患者の解剖学的差異を越えた姿勢非依存の空間一般化を達成する。
• 空間経路遵守を強制しつつ、患者努力に応じた実行速度適応を行う多モード混合制御フレームワークを提供する。
• Gaussian Mixture Regressionを用いた現場安全機構を開発し、異常な相互作用力を検知して軌道を可逆停止する。

提案手法

• Perception module uses YOLOv8-pose and Mediapipe Hand to extract 3D 6-DoF poses from RGB-D, constructing a body-centric frame anchored at the shoulder.
• Learning module encodes 6-DoF trajectories with Cartesian Dynamic Movement Primitives for scalable, posture-invariant motion.
• Execution module implements a decoupled spatial tunnel with an effort-based temporal dilation to couple progression to tangential patient effort.
• A Gaussian Mixture Regression model is learned on-the-fly from the patient’s limb to form a dynamic safety corridor and trigger trajectory reversals when forces exceed safe bounds.

実験結果

リサーチクエスチョン

• RQ1 Can therapist demonstrations captured in RGB-D be encoded into body-centric 6-DoF Cartesian DMPs that generalize across different patient anatomies?
• RQ2 Does the decoupled spatial-temporal control with an effort-based dilation ensure safe, self-paced execution across Passive, Active-Assisted, and Active-Resistive rehabilitation modes?
• RQ3 Can a patient-specific GMR model effectively detect abnormal interaction forces and safely reverses trajectories to prevent injury?

主な発見

Exercise	Reproduction Error (cm)
Elbow flexion–extension	1.8
Shoulder internal–external rotation	4.5
Shoulder abduction–adduction	4.9

• Average trajectory reproduction error of 3.7 cm across exercises and subjects.
• ROM error around 5.5 degrees on validated movements.
• Virtual tunnel with orthogonal admittance maintains spatial adherence under significant orthogonal forces (max spatial deviation 3.7 cm in baseline test).
• Force-based dilation enables Passive, Active-Assisted, and Active-Resistive modalities with self-paced progression.
• GMR-based safety corridor triggers smooth trajectory reversals within ~0.3 s of force violations, ensuring safe operation.