Talk 1 : Humanoid Multi-contact Locomotion for 3D Unstructured Environments

Speaker : Caspar Chung, Postdoctoral Associate in the A.I. Lab
Advisor : Oussama Khatib

Maintaining humanoid robot stability in unstructured environments is nontrivial because robots lack human-like tactile sensing and require complex task-specific controllers to integrate information from multiple sensors. To deploy humanoid robots in cluttered and unstructured environments such as disaster sites, it is necessary to develop advanced techniques in both locomotion and control. We proposes to incorporate a pair of actuated smart staffs with vision and force sensing that transforms biped humanoids into tripeds or quadrupeds or more generally, SupraPeds. The concept of SuprePeds not only improves the stability of humanoid robots while traversing rough terrain but also retains the manipulation capabilities. A unified task-oriented whole body robot formulation is also proposed to enable controls of task, posture, constraints, and balance in multiple contact situations. The simulation results are presented to demonstrate that the proposed control framework can efficiently deal with multi-contact locomotion in 3D unstructured environments.

Talk 2 : Towards the neural dynamics of motor feedback control

Speaker : Daniel O'Shea, PhD Candidate in Neuroscience
Advisor : Krishna Shenoy, Neural Prosthetic Systems Laboratory

Despite the apparent effortlessness with which we control our limbs, executing crisp and precise movements presents a complex control problem to the nervous system. During visually-guided reaching movements, neurons in motor cortex drive muscle activity via the spinal cord. Our understanding of how neural circuits generate the patterns of activity required to produce the desired movement is improving, but little is known regarding how motor cortex utilizes proprioceptive and visual feedback to adjust movements online. In order to study the neural mechanisms of motor feedback control, we aim to record neural activity in motor cortex during reaching movements in a simple virtual environment. A haptic feedback device renders visually- and haptically-defined obstacles and unexpected step-force perturbations, which will allow us to probe the cortical dynamics of proprioceptive feedback and the neural implementation of motor control policy.

Talk 3 : Closed-loop Stiffness and Damping Accuracy of Impedance-type Haptic Displays using Effective Impedances. 

Speaker : Nick Colonnese, PhD Candidate in Mechanical Engineering
Advisor : Allison Okamura

Impedance-type kinesthetic haptic displays aim to render arbitrary desired dynamics to a human operator using force feedback. To effectively render realistic virtual environments, the difference between desired and rendered dynamics must be small. In this talk, we analyze the closed-loop dynamics of haptic displays considering the effects of time delay and low-pass filtering. We identify important parameters for accuracy in terms of “effective impedances,” which express the closed-loop impedance as physical analogs. Our results establish bandwidth limits for rendering effective stiffness and damping, and the effects of time delay. Experimental data gathered with a Phantom Premium validates the theoretical analysis.

Talk 4 : Robot Control with Spiking Neurons

Speaker : Sam Fok, PhD Candidate in Electrical Engineering
Advisor : Kwabena Boahen

For the power budget of a laptop, the brain and spinal cord coordinate the movements of the human body. By reducing the power consumed in computation, the neuromorphic approach of emulating the brain's spiking neurons is a step towards building autonomous, biomimetic robots. With low-power, analog, spiking silicon neurons, we controlled a physical, 3 degree-of-freedom robot. Our approach is to construct a force-based, task-oriented controller and map its functional components onto the steady-state spiking activity of our silicon neuron hardware. With a force-based controller, our approach is compliant to external forces and safe for the operator and the environment. With a task-oriented controller, our approach is robust to unpredictable disturbances. We obtain the closed-form function of this controller and simplify the task of computing this function by factorizing it into a linear combination of several sub-functions. Each sub-function is then regressed on to the steady-state spiking response of a pool of silicon neurons. In operation, each pool of neurons is fed the current robot configuration and desired task forces and computes the joint torques necessary for the robot. Our neuromorphic system runs in real time and is the first such system to control a robot with three or more degrees-of-freedom.