Francesco Leofante

On the Synthesis of Guaranteed-Quality Plans for Robot Fleets in Logistics Scenarios via Optimization Modulo Theories

In manufacturing, the increasing involvement of autonomous robots in production processes poses new challenges on the production management. In this paper we report on the usage of Optimization Modulo Theories (OMT) to solve certain multi-robot scheduling problems in this area. Whereas currently existing methods are heuristic, our approach guarantees optimality for the computed solution. We do not only present our final method but also its chronological development, and draw some general observations for the development of OMT-based approaches.

Combining Static and Runtime Methods to Achieve Safe Standing-Up for Humanoid Robots

Due to its complexity, the standing-up task for robots is highly challenging, and often implemented by scripting the strategy that the robot should execute per hand. In this paper we aim at improving the approach of a scripted stand-up strategy by making it more stable and safe. To achieve this aim, we apply both static and runtime methods by integrating reinforcement learning, static analysis and runtime monitoring techniques.

Learning in Physical Domains: Mating Safety Requirements and Costly Sampling

Agents learning in physical domains face two problems: they must meet safety requirements because their behaviour must not cause damage to the environment, and they should learn with as few samples as possible because acquiring new data requires costly interactions. Active learning strategies reduce sampling costs, as new data are requested only when and where they are deemed most useful to improve on agents accuracy, but safety remains a standing challenge. In this paper we focus on active learning with support vector regression and introduce a methodology based on satisfiability modulo theory to prove that predictions are bounded as long as input patterns satisfy some preconditions. We present experimental results showing the feasibility of our approach, and compare our results with Gaussian processes, another class of kernel methods which natively provide bounds on predictions.

Optimal Multi-robot Task Planning: from Synthesis to Execution (and Back).

Integrated task planning and execution is a challenging problem with several applications in AI and robotics. In this work we consider the problem of generating and executing optimal plans for multirobot systems under temporal and ordering constraints. More specifically, we propose an approach that unites the power of Optimization Modulo Theories with the flexibility of an on-line executive, providing optimal solutions for task planning, and runtime feedback on their execution.

Task Planning with OMT: An Application to Production Logistics

Task planning is a well-studied problem for which interesting applications exist in production logistics. Planning for such domains requires to take into account not only feasible plans, but also optimality targets, e.g., minimize time, costs or energy consumption. Although there exist several algorithms to compute optimal solutions with formal guarantees, heuristic approaches are typically preferred in practical applications, trading certified solutions for a reduced computational cost. Reverting this trend represents a standing challenge within the domain of task planning at large. In this paper we discuss our experience using Optimization Modulo Theories to synthesize optimal plans for multi-robot teams handling production processes within the RoboCup Logistics League. Besides presenting our results, we discuss challenges and possible directions for future development of OMT planning.