This paper uses physiological measurements to estimate human workload and effort in physical human–robot interaction. Ten subjects performed 19 consecutive task periods using the ARMin robot while difficulty was varied along two scales. Three physiological modalities were measured: electroencephalography, autonomic nervous system (ANS) responses (electrocardiography, skin conductance, respiration, skin temperature) and eye tracking. After each task period, reference workload and effort values were collected using the NASA Task Load Index. Machine learning was used to estimate workload and effort from physiological data. All three physiological modalities performed significantly better than random, particularly using nonlinear estimation algorithms. The most important ANS responses were respiration and skin conductance, while the most important electroencephalographic information was obtained from frontal and central sites. However, all three physiological modalities were outperformed by task performance and movement data. This suggests that future studies should try to demonstrate advantages of physiological measurements over other information sources.