Towards biomimetic control of robotic or paralyzed limbs

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Our long-term objectives are to learn from biological movement and devise efficient and novel control strategies for artificial systems (robots, prostheses) and for assisted human control (FES, neural interfaces). To date, we have focused on two streams in parallel sub-objectives: 1- New analytical tools applicable to biological movement systems in the difficult context of non-linear parametric fields, switched hybrid strategies and imposed topologies. Funded by NSERC in the past, we produced software innovations now supported by NSERC/ I2I: to create automated classification of response modes in switched systems and identification tools to estimate the dynamics of each mode despite switching characteristics. They are targeted for distribution to Neuroscientists, Neuro-otologists and Clinicians in the field. 2- Novel models of biological control strategies for eye-head and ocular reflexes (funded by CIHR) based on the special nature (topology) of neural circuits in the brainstem and spinal cord. These models change the interpretation of data at both behavioural and neural levels, and suggest new protocols to test the integrity of motor systems in more natural mixed-sensory environments, as in daily life. This is only possible now because our algorithms can handle diverse conditions. More recently, explorations on arm reaching (funded by NSERC) predict that our control strategies used in eye-head coordination are consistent with primate arm or leg trajectories and the topology of the spinal cord. The result is much simpler control than typically used in the robotic literature – i.e. movement without planning. All of these systems use network topologies similar in their spatial organization and in the use of sensorimotor fusion. This suggests a possible general control theory for all platforms, be they stacked and rotatory, or segmental limbs. So the long-term goal is: TO FORMALIZE A BIOMIMETIC CONTROL STRATEGY FOR MOVING SYSTEMS THAT WILL ALLOW EXECUTION OF SIMPLE TASKS WITHOUT A-PRIORI TRAJECTORY PLANNINGAs short term-objectives, the mentioned characteristics found in biology will be evaluated and formalized for general applications, with:• A controller structure or topology analoguous to neural connections in the brainstem and spinal cord (symmetry, sites of sensor-motor interactions) to imbed dynamic modes with mode selection criteria (switch, order of mapping matrix) first spatially 1D then 3D.• Optimal Non-linear gain fields between sensors and their target platforms, and between platforms, which determine the dynamics and curvature of trajectories.• Learning strategies for controller “tuning” by executive central control (analogs cortex and cerebellum) and allow independent adjustments of movement speed and trajectory, without the need to re-compute a trajectory plan.The main assumptions are: task error sent directly to all participating platforms (no separate goals); trajectories for each segment and the end-point evolve as a property of the dynamics of the network, rather than pre-imposed by computation as in classical robotics. If this can be generalized and easily tuned for different mechanical systems, it would have impact in several areas: a 'natural feel' for interfaces that attempt to drive paralyzed limbs, a faster learning curve for the use of artificial limbs if the input from alternate muscles can preserve their natural activation patterns, and a more natural interface system for remote device operation. The goal is to allow the user to keep his/her habitual activation patterns, rather than develop new ones that may be in conflict with their neural capabilities. Finally, this should lead to smart robotic systems that adjust to contexts autonomously, with low computational demand.