Robotic interfaces can dynamically interact with humans performing movements and can be used to study neuromuscular adaptation. Such devices can produce computer controlled dynamics during movement and measure the interaction force and movement trajectory. On the other hand, functional magnetic resonance imaging (fMRI) can provide insight into the functional organization and plasticity of the brain. Using a robotic interface in conjunction with fMRI could allow neuroscientists to investigate the brain mechanisms of manipulation and motor learning, give therapists a tool for adaptive and patient-specific rehabilitation therapies, and assist medical doctors in functional diagnostics of motor dysfunctions. However, the MR environment imposes severe safety and electromagnetic compatibility constraints on mechatronic components, and the accessible workspace around the subject is limited. In addition, interaction with human motion must be safe and gentle. This thesis investigates the MR compatibility and performances of mechatronic elements, and develops an fMRI-compatible robotic technology consisting of sensors and actuators as well as adequate safety, control and synchronization strategies. It thus becomes possible to design various robotic systems for interaction with human motion during fMRI. This novel technology is benchmarked through the realization of several MR-compatible robotic systems which are currently being used by neuroscience groups in Japan and Europe. These include a highly MR-compatible interface for wrist motion, a tactile finger stimulation device using both intrinsically compatible and electrically powered components, as well as a two-degrees-of-freedom interface to investigate the control of multi-joint arm movements. The MR compatibility of these devices is successfully tested using a protocol developed for the particularly sensitive functional MRI sequences. In a further step towards performing multi-joint arm movements during fMRI, movement-related artifacts and biomechanical constraints are examined, and movements suitable for motor control studies are identified. Preliminary experiments with the realized systems demonstrate the potential of such robotic interfaces: the brain activation patterns during mental simulation of wrist motion are different from the patterns obtained during actual movements in interaction with the interface, and activation patterns in all conditions agree with results from literature. The well-controlled conditions as well as the recorded position and force data during fMRI open up new possibilities in data analysis and may allow new insights into the brain mechanisms involved in human motor control.