Overall, the AM-PAC-CAT's Basic Mobility scale demonstrated excellent psychometric properties while the Daily Activity scale demonstrated less adequate psychometric properties when applied in this outpatient sample. The mean length of time to complete the Basic Mobility scale was 1.9 minutes, using, on average, 6.6 items per CAT session, and the mean length of time to complete the Daily Activity scale was 1.01 minutes, using on average, 6.8 items. BACKGROUND AND CONCLUSION: Overall, the findings are encouraging, yet they do reveal several areas where the AM-PAC-CAT scales can be improved to best suit the needs of patients who are receiving outpatient orthopedic physical therapy of the type included in this study.

No abstract

No abstract

When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-rational choices, with a "rationality coefficient" capturing how much noise or entropy we expect to see in the human behavior. Prior work typically sets the rationality level to a constant value, regardless of the type, or quality, of human feedback. However, in many settings, giving one type of feedback (e.g. a demonstration) may be much more difficult than a different type of feedback (e.g. answering a comparison query). Thus, we expect to see more or less noise depending on the type of human feedback. In this work, we advocate that grounding the rationality coefficient in real data for each feedback type, rather than assuming a default value, has a significant positive effect on reward learning. We test this in both simulated experiments and in a user study with real human feedback. We find that overestimating human rationality can have dire effects on reward learning accuracy and regret. We also find that fitting the rationality coefficient to human data enables better reward learning, even when the human deviates significantly from the noisy-rational choice model due to systematic biases. Further, we find that the rationality level affects the informativeness of each feedback type: surprisingly, demonstrations are not always the most informative---when the human acts very suboptimally, comparisons actually become more informative, even when the rationality level is the same for both. Ultimately, our results emphasize the importance and advantage of paying attention to the assumed human-rationality-level, especially when agents actively learn from multiple types of human feedback.