In this thesis, we address the problem of planning, monitoring and learning in robotic systems, while considering the safety and time constraints.

Motion and action planning for robotic systems is important for real, physical world applications. Robots are capable of performing repetitive tasks at speeds and accuracies that far exceed those of human operators and are widely used in manufacturing, medical fields and even transportation.

Planning commonly refers to a process of converting high-level task specifications into low-level control commands that can be executed on the system of interest.

Time behavior is a most important issue for the autonomous systems of interest, and it is critical for many robotic tasks.

Most state of the art methods, however, are not capable of providing the framework needed for the autonomous systems to plan under finite time constraints.

Safety and time constraints are two important aspects for the plan. We are interested in the safety of the plan, such as Can the robot reach the goal without collision?''. We are also interested in the time constraints for the plan, such as Can the robot finish this task after 3 minutes but no later than 5 minutes?''. These type of tasks are important to understand in robot search and rescue or cooperative robotic production line.

In this thesis, we address these problems by two different approaches, the first one is a timed automata based approach, which focuses on a more high-level, abstracted result with less computational requirement. The other one involves converting the problem into a mixed integer linear programming (MILP) with more low-level control details but requires higher computational power. Both methods are able to automatically generate a plan that are guaranteed to be correct.

The robotic systems may behave differently in runtime and not able to execute the task perfectly as planned.

Given that a robotic system is naturally cyber-physical, and malfunctions can have safety consequences, monitoring the system’s behavior at runtime can be key to safe operation.

Therefore, it is important to consider both time and space tolerances during the planning phase, and also design runtime monitors for error detection and possible self-correction.

We provide an optimization-based formulation which takes the tolerances into account, and we have designed runtime monitors to monitor the status of the systems, as well as an event-triggered model predictive controller for self-correction.

Learning is another very important aspect for the robotics field. We hope to only provide the robot with high-level task specifications, and the robot learns to accomplish the task. Thus, in the next part of this thesis, we discussed how the robot could learn to accomplish task specified by metric interval temporal logic, and how the robot could replan and self-correct if the initial plan fails to execute correctly.

As the field of robotics is expanding from the fixed environment of a production line to complex human environments, robots are required to perform increasingly human-like manipulation tasks. Thus, for the last aspect of the thesis, we considered a manipulation task with dexterous robotic hand - Shadow Hand. We collected the multimodal haptic-vision dataset, and proposed the framework of self-assurance slippage detection and correction. We provided the simulation and also real-world implementation with a UR10 and Shadowhand robotic system.