This thesis focuses on making improvements to time-delayed teleoperation systems, with both direct and semi-autonomous haptic control, by addressing the challenges associated with force-position (F-P) predictive architectures. As the time delay from the communication channel increases, system stability and performance degrade. Previously, solutions focused on communication channel stability and environment force estimation methods that primarily rely on linearization of the Hunt-Crossley (HC) contact model. These result in a loss of transparency in the system and limiting use cases from linearization assumptions. Moreover, semi-autonomous solutions aimed at decreasing user effort and automating subtasks, such as obstacle avoidance and user guidance, require training or singularly focus on joint space tasks. This work addresses the shortcomings of the aforementioned methods by refocusing on system components to achieve more favorable dynamics during environment contact with the use of a series elastic actuator (SEA), investigating alternative HC parameter estimation techniques, and synthesizing an assistive semi-autonomous control framework that predicts user intention recognition and automates gross motion tasks. Experimental results with a remote SEA demonstrate improved performance with stiff environments in delays of up to two seconds round trip time. The coupling of the force and position through the actuator along with simultaneous sensing capabilities also show robustness for contact with soft environments. Further improvements with soft environment contact are achieved through HC parameter estimation, with smooth parameter update switching using a Sigmoid function. A novel application of Chebyshev polynomial approximation for adaptive parameter estimation of the HC model was also proposed. This approach provides control via backstepping with adaptive parameter estimation using Lyapunov methods. Additionally, this method reduces excitation requirements by using nonlinear swapping and the data accumulation concept to guarantee parameter convergence. A simulated teleoperation system demonstrates the effectiveness of this approach and initial results from experiment show promise for this approach in practice. Finally, a user study involving a pick and place task produced favorable results for the proposed semi-autonomous framework which significantly reduced task completion times. / Master of Science / Teleoperated systems are powerful solutions for remotely executing tasks in situations where autonomous solutions are not robust enough and/or user knowledge is desired for a task. However, teleoperation performance and stability is degraded by delays in the communication channel. A common way to deal with time delay is to use a predictive controller on the local side to cancel out the delay by knowing the remote side dynamics. Previous approaches have focused on stabilizing the communication channel or the use of estimators and observers to better capture the remote side dynamics. The drawback of these approaches is that they achieve stability at the expense of system transparency, leading to divergence in the force and position matching between the master and remote side. Many of the methods for environment force estimation involves linearizing contact models, creating limitations in their application. Moreover, semi-autonomous solutions aimed at decreasing user effort and automating subtasks such as obstacle avoidance and user guidance require training data sets for the algorithm or only focus individually on joint space tasks. This thesis addresses the shortcomings of the aforementioned methods by refocusing on system components to achieve more favorable dynamics using a series elastic actuator (SEA) while interacting with the environment, investigating nonlinear and linear contact model estimation methods for identifying parameters of the Hunt-Crossley (HC) model, and synthesising an assistive semi-autonomous control framework that predicts user intention for task execution. Experimental results for the use of an SEA demonstrate improved performance with stiff environments in delays of up to two seconds round trip time (RTT). The coupling of the force and position through the actuator along with simultaneous sensing capabilities also showed robustness for contact with soft environments. Various estimation methods for HC parameter identification was investigated to improve the local side model. A novel application of Chebyshev polynomial approximation of the HC model with adaptive parameter estimation was also proposed to provide control along with decreasing the excitation requirements by using backsteping control with nonlinear swapping and the data accumulation concept. A simulated teleoperation system demonstrated the effectiveness of this approach with a smooth paramater update transition. Initial results from experiment also show promise for this approach in practice. Finally, a user study involving a pick and place task produced favorable results for the proposed semi-autonomous framework which significantly reduced task completion times.