Robotic systems, such as prosthetics and exoskeletons, offer people suffering from motor impairments a chance to regain lost physical functionality. However, the neural control that individuals are able to exert over these robots is currently limited. This is due to both lack of control authority in many degrees of freedom and insufficient sensory feedback through the human-robot interface. We propose that haptic feedback is paramount for accurate and efficient control of robots via brain-computer interfaces (BCIs). Skin stretch at the fingertip is a novel form of haptic feedback for improving BCI-based robot control. In this paper, we describe the design of a BCI-driven skin-stretch device, assess several control paradigms for this device, and evaluate its effectiveness in a small user study. We show that BCI-based movement-intent classification improved in the presence of skin-stretch feedback for 3 of 4 healthy individuals controlling a computer cursor via an inexpensive, commercial electroencephalography-based (EEG) BCI.

Telerobotics has the potential to facilitate the repair of satellites in geosynchronous orbit by allowing human operators to interact naturally with remote objects. Time delays on the order of seconds make it difficult to provide immersive feedback to the operator, motivating the use of predictive visual and haptic displays of the robot and environment. A teleoperation framework developed for this scenario invokes a two-part environment model that predicts motion of objects in the environment, both in free space and during contact with the robot. When objects in the environment are in free space, a propagated model using delayed data provides predictive feedback to the operator. However, when the robot interacts with the environment, a local environment model that does not propagate delayed data is used. This reduces computational load and ensures stability during robot-environment interactions. Two experiments were carried out to test the teleoperation system. Results demonstrate the ability of the prediction algorithm to provide reliable feedback and improve operator performance before, during, and after robot-environment interactions.