Representing Robot Task Plans as Robust LogicalDynamical Systems It is difficult to create robust, reusable, and reactive behaviors for robots that can be easily extended and combined. Frameworks such as Behavior Trees are flexible but difficult to characterize, especially when designing reactions and recovery behaviors to consistently converge to a desired goal condition. We propose a framework which we call Robust LogicalDynamical Systems (RLDS), which combines the advantages of task representations like behavior trees with theoretical guarantees on performance. RLDS can also be constructed automatically from simple sequential task plans and will still achieve robust, reactive behavior in dynamic realworld environments. In this work, we describe both our proposed framework and a case study on a simple household manipulation task, with examples for how specific pieces can be implemented to achieve robust behavior. Finally, we show how in the context of these manipulation tasks, a combination of an RLDS with planning can achieve better results under adversarial conditions.

Planning Reactive Manipulation in Dynamic Environments When robots perform manipulation tasks, they need to determine their own movement, as well as how to make and break contact with objects in their environment. Reasoning about the motions of robots and objects simultaneously leads to a constrained planning problem in a highdimensional statespace. Additionally, when environments change dynamically motions must be computed in realtime.To this end, we propose a feedback planner for manipulation. We model manipulation as constrained motion and use this model to automatically derive a set of constraintbased controllers. These controllers are used in a switchingcontrol scheme, where the active controller is chosen by a reinforcement learning agent. Our approach is capable of addressing tasks with secondorder dynamics, closed kinematic chains, and timevariant environments. We validated our approach in simulation and on a real, dualarm robot. Extensive simulation of three distinct robots and tasks show a significant increase in robustness compared to a previous approach.