Human-robot collaboration (HRC) has been used in a wide range of applications, such as industry, healthcare, and even in daily household activities to assist humans with tasks such as cooking and cleaning. To make HRC more adaptable to real-world environments, robots must be able to interpret explicit and implicit instructions from environmental cues, then communicate solutions to effectively mimic human teamwork. While advancements have been made in training robots with more flexible teamwork skills, there are still challenges in implementing more human-like collaboration where robots can understand implicit verbal contexts and work through cases with multiple solutions. Previous work by Dr. Bashira Akter Anima at the University of Nevada, Reno used a collaborative architecture where robots act on word associations and respond accordingly for one-solution scenarios. For instance, the robot would begin making tea if the human said phrases such as, “I am thirsty” or “It is cold outside.” This work extends the hierarchical robot control architecture from Dr. Anima’s work by introducing a new skill to handle scenarios with multiple solutions available in the environment. To help the robot work through multiple solutions, it will be given a sample dialog structure with a modified task architecture that considers a human answer to choose the correct skill to perform. By the end of this work, the humanoid robot Baxter is expected to engage in dialogue with humans to resolve ambiguities and determine the preferred task, mimicking human teamwork. Using this extended task architecture, Baxter will execute agreed actions, enhancing adaptability in real-world environments. For example, if a human says, "It’s hot outside," the robot might suggest cold tea or sunscreen, discuss options, and perform the chosen task while adhering to constraints.