Contact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control. A promising strategy is to develop policies that are robust across a wide range of parameters. Domain adaptation and domain randomization are widely used, but they tend to either limit generalization to new instances or perform conservatively due to neglecting instance-specific information. Explicit motor adaptation addresses these issues by estimating system parameters online and then retrieving the parameter-conditioned policy from a parameter-augmented base policy. However, it typically requires precise system identification or additional training of a student policy, both of which are challenging in contact-rich manipulation tasks with diverse physical parameters. In this work, we propose implicit motor adaptation , which enables parameter-conditioned policy retrieval given a roughly estimated parameter distribution instead of a single estimate. We leverage tensor train as an implicit representation of the base policy, facilitating efficient retrieval of the parameter-conditioned policy by exploiting the separable structure of tensor cores. This framework eliminates the need for precise system estimation and policy retraining while preserving optimal behavior and strong generalization. We provide a theoretical analysis to validate the approach, supported by numerical evaluations on three contact-rich manipulation primitives. Both simulation and real-world experiments demonstrate its ability to generate robust policies across diverse instances. Project website: https://sites.google.com/view/implicit-ma .