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 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![License](https://img.shields.io/github/license/soraxas/sbp-env.svg)](https://github.com/soraxas/sbp-env/blob/master/LICENSE)
 
-Sampling-based motion planners' testing environment (`sbp-env`) is a full feature framework to quickly test different sampling-based algorithm for motion planning. `sbp-env` focuses on flexibility of tinkering with different aspect of the framework, and had divided the main planning components into two categories (i) **samplers** and (ii) **planners**.
+Sampling-based motion planners' testing environment (`sbp-env`) is a full feature framework to quickly test different sampling-based algorithms for motion planning. `sbp-env` focuses on the flexibility of tinkering with different aspects of the framework, and had divided the main planning components into two categories (i) **samplers** and (ii) **planners**.
 
-The focus of *motion planning planning research* had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routine to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.
+The focus of *motion planning research* had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routines to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.
 
 Have a look at the [documentations](https://cs.tinyiu.com/sbp-env) for more detail information. If you are looking for the previous code for the RRdT* paper it is now archived at [soraxas/rrdt](https://github.com/soraxas/rrdt).