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This paper introduces Synchronous Action Monitoring with Token Destruction (SAM-TD), a novel compilation method that integrates Linear Temporal Logic over finite traces ($\textrm{LTL}_f$) specifications into stream-based Task and Motion Planning (TAMP). By translating $\textrm{LTL}_f$ constraints into automata and embedding them into action schemas, SAM-TD effectively manages dynamically generated objects during planning without the need for a fixed object set. Experimental results demonstrate SAM-TD's capability to handle temporal constraints in stream-based TAMP across three robotics environments, achieving competitive performance against existing temporal-constraint compilation methods on standard benchmarks.
SAM-TD enables stream-based robotics planning to adhere to complex temporal constraints, a capability previously unattainable in this domain.
Stream-based robotics Task and Motion Planning (TAMP) integrates discrete symbolic planning with dynamically generated continuous geometric parameters, such as poses, grasps, and trajectories. However, stream-based planners typically reason only about goal reachability, whereas long-horizon tasks also demand adherence to temporal specifications, such as safety-critical ordering, invariance, and liveness constraints. No methods currently exist to enforce such temporal constraints for stream-based solvers because streams generate an expanding geometric object set via iterative stream refinement loops during planning, rendering existing temporal-logic compilation techniques incompatible. We therefore present Synchronous Action Monitoring with Token Destruction (SAM-TD), a compilation method that enforces arbitrary Linear Temporal Logic over finite traces ($\textrm{LTL}_f$) specifications in stream-based TAMP. SAM-TD translates arbitrary $\textrm{LTL}_f$ constraints into automata and embeds regressed automaton guards into action schemas, which are pre-specified before planning begins. By doing so, SAM-TD can handle objects generated by streams during planning, thus circumventing the need to enumerate a fixed object set or modify the underlying planner. During search, SAM-TD synchronously updates automaton states and uses a validity token shared across all automata to prune constraint-violating branches. We show that SAM-TD supports dynamically generated stream objects from iterative stream refinements during plan search. Experimental results provide the first ever demonstration of stream-based TAMP under $\textrm{LTL}_f$ constraints in three robotics PDDLStream environments. Furthermore, on standard discrete PDDL benchmarks, SAM-TD is competitive with state-of-the-art temporal-constraint compilation methods.
