DeepAI AI Chat
Log In Sign Up

Interactive Robot Transition Repair With SMT

by   Jarrett Holtz, et al.

Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states. Since transitions depend on physical parameters, when the environment changes, a roboticist has to painstakingly readjust the parameters to work in the new environment. We present interactive SMT-based Robot Transition Repair (SRTR): instead of manually adjusting parameters, we ask the roboticist to identify a few instances where the robot is in a wrong state and what the right state should be. A lightweight automated analysis of the transition function's source code then 1) identifies adjustable parameters, 2) converts the transition function into a system of logical constraints, and 3) formulates the constraints and user-supplied corrections as MaxSMT problem that yields new parameter values. Our evaluation shows that SRTR is effective on real robots and in simulation. We show that SRTR finds new parameters 1) quickly, 2) with only a few corrections, and 3) that the parameters generalize to new scenarios. We also show that a simple state machine corrected by SRTR can out-perform a more complex, expert-tuned state machine in the real world.


SMT-based Robot Transition Repair

State machines are a common model for robot behaviors. Transition functi...

Practical Integer Overflow Prevention

Integer overflows in commodity software are a main source for software b...

Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo

Rather than programming, training allows robots to achieve behaviors tha...

TraceFixer: Execution Trace-Driven Program Repair

When debugging unintended program behavior, developers can often identif...

Rule-based Shielding for Partially Observable Monte-Carlo Planning

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online a...

REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer

A popular paradigm in robotic learning is to train a policy from scratch...