Representing Robot Task Plans as Robust Logical-Dynamical 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 Logical-Dynamical 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 real-world 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.

Authors

• Chris Paxton 0001
• Nathan D. Ratliff
• Clemens Eppner
• Dieter Fox

Keywords

• Planning
• Intelligent robots
• Robust System
• Task Planning
• Simple Task
• Behavioral Reactions
• Manipulation Tasks
• Behavioral Recovery
• Task Representations
• Robust Behavior
• Preconditioning
• Transition State
• State Space
• Binary Data
• Simple Algorithm
• Continuous State
• Path Planning
• State Machine
• Goal State
• Tree Search
• Current Operation
• Markov Property
• Logic State
• Implicit Condition
• Conditional Logic
• Downstream Operations
• Current Pose
• First-order Logic
• Logical Constraints
• Broken Ends
• True Value