Getting confidence intervals for robust regression coefficient (MASS::rlm)

Is there any possible way to get 95% CI for regression coefficients from the robust regression, as implemented in MASS::rlm?

# libraries needed
library(MASS)
library(stats)
library(datasets)

# running robust regression
(x <-
  MASS::rlm(formula = scale(Sepal.Length) ~ scale(Sepal.Width),
            data = iris))
#> Call:
#> rlm(formula = scale(Sepal.Length) ~ scale(Sepal.Width), data = iris)
#> Converged in 5 iterations
#> 
#> Coefficients:
#>        (Intercept) scale(Sepal.Width) 
#>        -0.03728607        -0.14343268 
#> 
#> Degrees of freedom: 150 total; 148 residual
#> Scale estimate: 1.06

# getting confidence interval for the regression coefficient
stats::confint(object = x,
               parm = "scale(Sepal.Width)",
               level = 0.95)
#>                    2.5 % 97.5 %
#> scale(Sepal.Width)    NA     NA

Explicitly calling confint.default seems to provide good results, like this:

confint.default(object = x, parm = "scale(Sepal.Width)", level = 0.95)

#                        2.5 %     97.5 %
#scale(Sepal.Width) -0.3058138 0.01894847

---

Edit

confint uses method confint.lm when it is passed x because x is of class lm (as well as rlm). Calling confint.default explicitly avoids this. These two functions only differ in one line of code, as shown below:

confint.lm

fac <- qt(a, object$df.residual)

confint.default

fac <- qnorm(a)

The issue is that x$df.residual is NA and, consequently, qt(a, object$df.residual) produces an NA whereas qnorm(a) doesn't have this problem.