UCLA

Although robotic locomotion and manipulation have shown some remarkable progress in the real world, the current locomotion and manipulation algorithms are inefficient in performance. They often only work for relatively simple tasks such as walking and running for locomotion and pick-and-place in structured environments (e.g., factory) for manipulation. In contrast, humans can perform quite dexterous tasks through contact as contacts provide additional dexterity to interact with environments. Hence, understanding the underlying contact mechanics plays a key role in designing contact-aware planners, controllers, and estimators for locomotion and manipulation.

However, design for planners, controllers, and estimators is extremely challenging. First, the number of contact states such as making and breaking contact with environments increases dramatically as the number of contacts increases. Thus, the underlying contact dynamics become large-scale non-smooth dynamics. As a result, optimization solvers have difficulties converging due to the non-convexity of the optimization problem.

Second, it is desirable that a robot should be able to interact in unknown environments during operation, leading to generalizable locomotion and manipulation. However, robust planning with frictional interaction with uncertain physical properties is very tough as the robot might cause undesired unexpected contact events. As a result, a robot might not be able to complete its desired task.

Third, once uncertainty is quite large, it is indispensable for closed-loop controllers to stabilize locomotion and manipulation. However, the design of manipulation is quite challenging as most manipulation systems are underactuated and unobservable with potential changes in contact states and modes.

In this dissertation, we present a methodology for contact-rich locomotion and planning using trajectory optimization. We first show that the planner using graph-search planners with trajectory optimization can be beneficial for decreasing the computation complexity. Second, we describe our contact-implicit trajectory optimization for planning of multi-limbed systems for running and climbing. We use decomposition-based optimization techniques to efficiently design a trajectory for a robot subject to various complicated contact constraints such as mixed-integer constraints. Then, we present our robust and stochastic trajectory optimization algorithms for multi-contact systems. We show that our chance-constrained optimization is applicable for planning multi-limbed robots. We also propose covariance steering algorithm for contact-rich systems using a particle filter to approximate a distribution of underlying contact dynamics. Our covariance steering is able to regulate robots' states and contact states simultaneously with probabilistic guarantees. Furthermore, utilizing the underlying structure of contact-rich manipulation, we present robust bilevel trajectory optimization for pivoting manipulation under uncertain physical parameters such as friction coefficients. Our proposed framework is able to design optimal control sequences while improving the worst-case stability margin along the manipulation. Finally, we present our closed-loop controller framework for tool manipulation using visuo-tactile feedback. Our approach enables the robot to achieve tool manipulation under unexpected contact events in closed-loop control fashion with no visual feedback for partially unknown objects.

The perspectives gained from this dissertation provide better insight into developing a contact-rich planning, estimation, and control framework for dexterous locomotion and manipulation in highly unstructured environments.