Took me a bit to figure out what 'valid recognized state' even meant in context.
Clarify the definition of a 'valid recognized state' (e.g., returns to origin, faces original direction, or both) and the instruction set. Then simulate the robot's movement using coordinate tracking and direction state, handling edge cases like empty input, invalid characters, and large sequences.
Pro tip: Before coding, explicitly state your assumptions about the instruction set and what constitutes a valid state; this shows you think about ambiguity and requirements, which interviewers value highly.
Ask the interviewer to define the instruction set (e.g., 'U', 'D', 'L', 'R') and what 'valid recognized state' means (e.g., returns to origin, faces original direction, or both). Confirm edge cases like empty input or invalid characters.
Use two integers (x, y) to track position and a variable to track direction (e.g., 0 for north, 1 for east, etc.). Alternatively, use a set of visited positions if the valid state involves revisiting a location.
Iterate through each instruction, updating position and direction accordingly. For example, 'L' and 'R' change direction, while 'U', 'D', 'L', 'R' move the robot if they represent absolute moves.
After processing all instructions, compare the final state against the defined valid state. Return true if it matches, false otherwise.
Test with empty string, single instruction, invalid characters, and very long sequences. Discuss time and space complexity (O(n) time, O(1) space).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.