1 research outputs found
SMT-based Robot Transition Repair
State machines are a common model for robot behaviors. Transition functions
often rely on parameterized conditions to model preconditions for the
controllers, where the correct values of the parameters depend on factors
relating to the environment or the specific robot. In the absence of specific
calibration procedures a roboticist must painstakingly adjust the parameters
through a series of trial and error experiments. In this process, identifying
when the robot has taken an incorrect action, and what should be done is
straightforward, but finding the right parameter values can be difficult. We
present an alternative approach that we call, interactive SMT-based Robot
Transition Repair. During execution we record an execution trace of the
transition function, and we ask the roboticist to identify a few instances
where the robot has transitioned incorrectly, and what the correct transition
should have been. A user supplies these corrections based on the type of error
to repair, and an automated analysis of the traces partially evaluates the
transition function for each correction. This system of constraints is then
formulated as a MaxSMT problem, where the solution is a minimal adjustment to
the parameters that satisfies the maximum number of constraints. In order to
identify a repair that accurately captures user intentions and generalizes to
novel scenarios, solutions are explored by iteratively adding constraints to
the MaxSMT problem to yield sets of alternative repairs. We test with state
machines from multiple domains including robot soccer and autonomous driving,
and we evaluate solver based repair with respect to solver choice and
optimization hyperparameters. Our results demonstrate that SRTR can repair a
variety of states machines and error types 1) quickly, 2) with small numbers of
corrections, while 3) not overcorrecting state machines and harming generalized
performance.Comment: In submission to AIJ. arXiv admin note: text overlap with
arXiv:1802.0170