171 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks
Autonomous robots are increasingly utilized in realistic scenarios with
multiple complex tasks. In these scenarios, there may be a preferred way of
completing all of the given tasks, but it is often in conflict with optimal
execution. Recent work studies preference-based planning, however, they have
yet to extend the notion of preference to the behavior of the robot with
respect to each task. In this work, we introduce a novel notion of preference
that provides a generalized framework to express preferences over individual
tasks as well as their relations. Then, we perform an optimal trade-off
(Pareto) analysis between behaviors that adhere to the user's preference and
the ones that are resource optimal. We introduce an efficient planning
framework that generates Pareto-optimal plans given user's preference by
extending A* search. Further, we show a method of computing the entire Pareto
front (the set of all optimal trade-offs) via an adaptation of a
multi-objective A* algorithm. We also present a problem-agnostic search
heuristic to enable scalability. We illustrate the power of the framework on
both mobile robots and manipulators. Our benchmarks show the effectiveness of
the heuristic with up to 2-orders of magnitude speedup.Comment: 8 pages, 4 figures, to appear in International Conference on
Intelligent Robots and Systems (IROS) 202
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems
In the field of Automated Planning there is often the need for a set of
planning problems from a particular domain, e.g., to be used as training data
for Machine Learning or as benchmarks in planning competitions. In most cases,
these problems are created either by hand or by a domain-specific generator,
putting a burden on the human designers. In this paper we propose NeSIG, to the
best of our knowledge the first domain-independent method for automatically
generating planning problems that are valid, diverse and difficult to solve. We
formulate problem generation as a Markov Decision Process and train two
generative policies with Deep Reinforcement Learning to generate problems with
the desired properties. We conduct experiments on several classical domains,
comparing our method with handcrafted domain-specific generators that generate
valid and diverse problems but do not optimize difficulty. The results show
NeSIG is able to automatically generate valid problems of greater difficulty
than the competitor approaches, while maintaining good diversity
Spatial Reasoning via Deep Vision Models for Robotic Sequential Manipulation
In this paper, we propose using deep neural architectures (i.e., vision
transformers and ResNet) as heuristics for sequential decision-making in
robotic manipulation problems. This formulation enables predicting the subset
of objects that are relevant for completing a task. Such problems are often
addressed by task and motion planning (TAMP) formulations combining symbolic
reasoning and continuous motion planning. In essence, the action-object
relationships are resolved for discrete, symbolic decisions that are used to
solve manipulation motions (e.g., via nonlinear trajectory optimization).
However, solving long-horizon tasks requires consideration of all possible
action-object combinations which limits the scalability of TAMP approaches. To
overcome this combinatorial complexity, we introduce a visual perception module
integrated with a TAMP-solver. Given a task and an initial image of the scene,
the learned model outputs the relevancy of objects to accomplish the task. By
incorporating the predictions of the model into a TAMP formulation as a
heuristic, the size of the search space is significantly reduced. Results show
that our framework finds feasible solutions more efficiently when compared to a
state-of-the-art TAMP solver.Comment: 8 pages, 8 figures, IROS 202
Probabilistic contingent planning based on HTN for high-quality plans
Deterministic planning assumes that the planning evolves along a fully
predictable path, and therefore it loses the practical value in most real
projections. A more realistic view is that planning ought to take into
consideration partial observability beforehand and aim for a more flexible and
robust solution. What is more significant, it is inevitable that the quality of
plan varies dramatically in the partially observable environment. In this paper
we propose a probabilistic contingent Hierarchical Task Network (HTN) planner,
named High-Quality Contingent Planner (HQCP), to generate high-quality plans in
the partially observable environment. The formalisms in HTN planning are
extended into partial observability and are evaluated regarding the cost. Next,
we explore a novel heuristic for high-quality plans and develop the integrated
planning algorithm. Finally, an empirical study verifies the effectiveness and
efficiency of the planner both in probabilistic contingent planning and for
obtaining high-quality plans.Comment: 10 pages, 1 figur
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