Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)

Counterexample Guided Inductive Optimization

This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the…

Counterexample-Driven Synthesis for Probabilistic Program Sketches

Probabilistic programs are key to deal with uncertainty in e.g. controller synthesis. They are typically small but intricate. Their development is complex and error prone requiring quantitative reasoning over a myriad of alternative designs. To mitigate this complexity, we adopt counterexample-guided inductive synthesis (CEGIS) to automatically synthesise finite-state probabilistic programs. Our approach leverages efficient model checking, modern SMT solving, and counterexample generation at program level. Experiments on practically relevant case studies show that design spaces with millions o…

Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions

Integrated Task and Motion Planning (TMP) provides a promising class of approaches for solving robot planning problems with intricate symbolic and geometric constraints. However, the practical usefulness of TMP planners is limited by their need for symbolic abstractions of robot actions, which are difficult to construct even for experts. We propose an approach to automatically construct and continuously improve a symbolic abstraction of a robot action via observations of the robot performing the action. This approach, called automatic abstraction repair, allows symbolic abstractions to be init…

k-诱导神经屏障证书用于未知非线性动力学

k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics

本文提出了一种基于神经网络的k-诱导神经屏障证书(k-NBCs)，用于部分未知的非线性系统，通过利用神经网络的可扩展性以及泛化Willems等人基本引理，构建数据驱动的表示以进行SMT验证，同时提高了设计灵活性。

形态等变流匹配用于双臂移动操作

Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation

本文提出利用形态对称性作为诱导偏倚，通过流匹配政策提升双臂移动操作的样本效率和零样本泛化能力。

Regularized Deep Signed Distance Fields for Reactive Motion Generation

Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and manufacturing settings is their ability to evaluate online and real-time collisions with the world around them. Distance-based constraints are fundamental for enabling robots to plan their actions and act safely, protecting both humans and their hardware. However, different applications require different distance resolutions, leading to various heuristic approaches for measuring distance fields w.r.t. obstacles, which a…