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Robot-assisted therapy devices are available for rehabilitation of persons after stroke, which is the leading cause of disability among adults in the United States (AHA 2006, Volpe et al. 2002). Improving upper extremity function after stroke is critical for performance of one’s life-role and the completion of unilateral and bilateral activities of daily living (ADLs). Carryover to real-life activities after rehabilitation training cannot be assumed (Sterr et al. 2000; Maclean et al. 2000; Ma and Trombly 2002; Trombly and Ma 2002; Prange et al. 2006). For example, the existence of learned non-use behavior indicates that motor gains after rehabilitation therapies may not transfer to long-term functioning on ADLs (Taub et al. 1994; Taub et al. 1999; Sterr et al. 2000). This behavior is present when persons with hemiparesis due to strokes demonstrate significant differences between residual movement capabilities and spontaneous use of the impaired arm in real world. There is a need to address barriers to the carryover of motor gains during training to stroke function in real life. This chapter reviews examples of current upper arm robot-assisted therapy environments and present findings from case study experiments with a new task-oriented, robot therapy system focused on improving carryover of motor improvements to functional activities of daily living. We draw attention to influence of function on arm movements during robot training and explore how future environments can be more functional and engaging. Robot-assisted therapy devices are now being used more frequently in the rehabilitation of persons with physical disabilities due to neurological trauma caused by stroke and spinal cord injury. These therapy robots provide semi- or fully-autonomous training and permit patients using them to engage in repeated and intense practice of goal-directed tasks (Volpe et al 2002; Prange et al 2006; Burgar et al. 2000; Loureiro et al. 2003; Patton et al. 2006; Krebs et al. 2003; MacClellan et al. 2005, Kahn et al. 2006). Typically, the automation of therapeutic exercises involves generating trajectories that guide reaching movements and the application of forces directly or indirectly to the impaired arm to assist, resist, and/or passively support it during the reaching exercise. For example, the MIT-MANUS (Krebs et