Unsynthesizable Cores - Minimal Explanations for Unsynthesizable High-Level Robot Behaviors

09/04/2014
by   Vasumathi Raman, et al.
0

With the increasing ubiquity of multi-capable, general-purpose robots arises the need for enabling non-expert users to command these robots to perform complex high-level tasks. To this end, high-level robot control has seen the application of formal methods to automatically synthesize correct-by-construction controllers from user-defined specifications; synthesis fails if and only if there exists no controller that achieves the specified behavior. Recent work has also addressed the challenge of providing easy-to-understand feedback to users when a specification fails to yield a corresponding controller. Existing techniques provide feedback on portions of the specification that cause the failure, but do so at a coarse granularity. This work presents techniques for refining this feedback, extracting minimal explanations of unsynthesizability.

READ FULL TEXT
research
04/11/2023

Resolving Ambiguity via Dialogue to Correct Unsynthesizable Controllers for Free-Flying Robots

In situations such as habitat construction, station inspection, or coope...
research
03/04/2019

Toward Achieving Formal Guarantees for Human-Aware Controllers in Human-Robot Interactions

With the primary objective of human-robot interaction being to support h...
research
03/02/2021

Reachability-based Identification, Analysis, and Control Synthesis of Robot Systems

We introduce reachability analysis for the formal examination of robots....
research
04/07/2022

Controlling Golog Programs against MTL Constraints

While Golog is an expressive programming language to control the high-le...
research
12/06/2017

Accomplishing High-Level Tasks with Modular Robots

The advantage of modular self-reconfigurable robot systems is their flex...
research
02/04/2022

Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications

There are spatio-temporal rules that dictate how robots should operate i...
research
06/22/2020

Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems

Machine learning becomes increasingly important to tune or even synthesi...

Please sign up or login with your details

Forgot password? Click here to reset