This project generally explores the problem of Task and Motion Planning (TAMP)—the hierarchical planning problem arising when the task is defined at a high-level, symbolically, while the solution is defined at a lower-level, geometrically. It is a key problem in modern robotics, which applies to any autonomous robot tasked by a human.
The broad goal of this project is to design efficient solution paradigms, and, by such, push the envelope of problems we can solve, leading to more capable robots. This also encapsulates advancing task planning and motion planning independently.
Unpacking
A recent preprint (Suprun et al., 2026) introduced a new paradigm for efficient planning with non-classical, temporally-extended goals, such as tasks defined using finite Linear Temporal Logic (LTLf). This work shows that such a problem can be solved hierarchically, TAMP-style, without computing the expensive “product automaton” of the task and the planning domain (as is the standard). This endeavour bridges Logic and Formal Methods with Planning.
A tower rearrangement planning problem.
References
2026
Preprint
TIDE: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals
Yuliia
Suprun, Khen
Elimelech, Lydia E.
Kavraki, and Moshe Y.
Vardi
Task planning with temporally extended goals (TEGs) is a critical challenge in AI and robotics, enabling agents to achieve complex sequences of objectives over time rather than addressing isolated, immediate tasks. Linear Temporal Logic on finite traces (LTLf ) provides a robust formalism for encoding these temporal goals. Traditional LTLf task planning approaches often transform the temporal planning problem into a classical planning problem with reachability goals, which are then solved using off-the-shelf planners. However, these methods often lack informed heuristics to provide a guided search for temporal goals. We introduce TIDE (Trace-Informed Depth-first Exploration), a novel approach that addresses this limitation by decomposing a temporal problem into a sequence of smaller, manageable reach-avoid sub-problems, each solvable using an off-the-shelf planner. TIDE identifies and prioritizes promising automaton traces within the domain graph, using cost-driven heuristics to guide exploration. Its adaptive backtracking mechanism systematically recovers from failed plans by recalculating costs and penalizing infeasible transitions, ensuring completeness and efficiency. Experimental results demonstrate that TIDE achieves promising performance and is a valuable addition to the portfolio of planning methods for temporally extended goals.
@article{Suprun26tide,bibtex_show=true,author={Suprun, Yuliia and Elimelech, Khen and Kavraki, Lydia E. and Vardi, Moshe Y.},author+an={2=KE},title={{TIDE}: A Trace-Informed Depth-First Exploration for Planning with Temporally Extended Goals},journal={arXiv:2601.12141},year={2026},keywords={preprint}}
