Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task. This thesis explores the coordination between the human-partners through a virtual setup involving continuous visual feedback. The interaction and coordination are modeled as a two-step process: 1) Collecting data for a collaborative couch-pushing task, where both the people doing the task have complete information about the goal but are unaware of each other's cost functions or intentions and 2) processing the emergent behavior from complete information and fitting a model for this behavior to validate a mathematical model of agent-behavior in multi-agent collaborative tasks. The baseline model is updated using different approaches to resemble the trajectories generated by these models to human trajectories. All these models are compared to each other. The action profiles of both the agents and the position and velocity of the manipulated object during a goal-oriented task is recorded and used as expert-demonstrations to fit models resembling human behaviors. Analysis through hypothesis teasing is also performed to identify the difference in behaviors when there are complete information and information asymmetry among agents regarding the goal position.