MEAM Ph.D. Thesis Defense: “Controlling Contact Transitions for Dynamic Robots”

Legged robots, robotic manipulators, and their combined embodiment as humanoid robots have received considerable attention across both academia and industry. However, with few notable exceptions, state-of-the-art demonstrations are significantly less dynamic than their biological counterparts. A considerable challenge towards achieving more dynamic robots lies within controlling contact interactions with their environment. Legged robots undergoing impacts experience near-instantaneous changes in their velocities, making accurate state estimation difficult and resulting in controller sensitivity to even small deviations in impact timing. Contact transitions are also challenging for robot manipulation due to the combinatorial complexity of planning across multiple contact modes. Frictional contact that often arises from dynamic manipulation further increases this planning complexity due to the introduction of additional contact modes and increased degree of underactuation.

To address these limitations, this thesis proposes algorithmic and systems contributions to gracefully handle contact transitions for dynamic robots. First, we identify that uncertainties from impact events enter the system dynamics in a structured manner. We leverage this structure to propose a general modification to model-based feedback controllers, enabling selective robustness to impact uncertainty while maximally retaining control authority. We validate our approach on custom dynamic jumping and running controllers on the 3D bipedal robot, Cassie.

Then, we examine dexterous dynamic manipulation through complex non-prehensile tasks that require considering the full spectrum of hybrid contact modes. We leverage recent advancements in contact-implicit MPC to generate contact-rich motion plans in real-time. We demonstrate, through careful integration of the MPC and low-level tracking controller, how contact-implicit MPC can be adapted to dynamic tasks. We perform two distinct tasks using the same model, notably without common aids such as reference trajectories or motion primitives, highlighting the generality of our approach.