Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learning
This paper investigates the application of Minimal Observation Inverse Reinforcement Learning (MO-IRL) to model and predict human arm-reaching movements with time-varying cost weights. Using a planar two-link biomechanical model and high-resolution motion-capture data from subjects performing a pointing task, we segment each trajectory into multiple phases and learn phase-specific combinations of seven candidate cost functions. MO-IRL iteratively refines cost weights by scaling observed and generated trajectories in the maximum entropy IRL formulation, greatly reducing the number of required demonstrations and convergence time compared to classical IRL approaches. Training on ten trials per posture yields average joint-angle Root Mean Squared Errors (RMSE) of 6.4 deg and 5.6 deg for six- and eight-segment weight divisions, respectively, versus 10.4 deg using a single static weight. Cross-validation on remaining trials and, for the first time, inter-subject validation on an unseen subject's 20 trials, demonstrates comparable predictive accuracy, around 8 deg RMSE, indicating robust generalization. Learned weights emphasize joint acceleration minimization during movement onset and termination, aligning with smoothness principles observed in biological motion. These results suggest that MO-IRL can efficiently uncover dynamic, subject-independent cost structures underlying human motor control, with potential applications for humanoid robots.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningSimilar Papers 제목 키워드 기반
Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or posture-specific optimization criteria. Usin…
Reinforcement LearningGeneralized Inverse Optimal Control and its Application in Biology
Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying phy…
Anomaly Detection using Principles of Human Perception
In the fields of statistics and unsupervised machine learning a fundamental and well-studied problem is anomaly detection. Anomalies are difficult to define, yet many algorithms have been proposed. Underlying the approac…
Anomaly DetectionUnsupervised Anomaly DetectionPhysics of Learning: A Lagrangian perspective to different learning paradigms
We study the problem of building an efficient learning system. Efficient learning processes information in the least time, i.e., building a system that reaches a desired error threshold with the least number of observati…
Reinforcement LearningMollified Value Learning
Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains challenging under limited state-action coverage. Existing physics-informed…
Representation LearningReinforcement Learning