The applicability of robotic automation has transcended the industrial domain through the emergence of collaborative robotics and is increasingly entering the realm of applications with high levels of physical human-robot interactions. This is concomitant with a paradigm shift towards higher force control sensitivity to accomplish functional and safety requirements concerning the regulation of contact forces between robots and humans. A fundamental challenge in this regard is the observability and estimation of interaction forces. Utilizing the availability of joint position and torque sensors in recent collaborative robot models that yield a larger perceptive field for interaction forces than local force sensors, a proprioceptive approach is taken in this thesis to develop inverse dynamic models to estimate dynamic disturbances and determine external interaction forces during fine-scale motion. A series of state-of-the-art techniques are implemented and evaluated on the KUKA LBR iiwa 14, including dynamic parameter identification, neural-network based single-step, and time-series models, and a novel hybrid architecture combining a rigid body dynamics model with downstream neural networks and joint rotational displacement encodings. The results indicate that significant improvements in torque and force estimation accuracy can be obtained by the proposed method when compared with conventional rigid body dynamics models or neural networks alone.