SPECTRA: Gated Spectral Policies for Force-Aware Manipulation

Robot manipulation requires smooth movements as well as precise adjustments. Force and tactile feedback can help a robot understand its interaction with an object, especially when visual observations alone are insufficient. However, this feedback is not equally useful throughout a task. Similarly, predicting finer motion details can help the robot make necessary corrections, but can also reproduce noise in demonstrations. An effective policy should therefore learn when force feedback and finer motion are useful. We introduce SPECTRA, a force-aware spectral diffusion policy that makes this choice during execution. It combines a coarse policy for smooth, low-frequency motion with a fine policy that uses additional force/tactile feedback and can predict the full range of motion frequencies. A learned gate uses recent force history to select the policy that generates the next action chunk. Experiments on simulated and real-robot manipulation tasks show that SPECTRA improves real-robot task success by up to 20 percentage points over the strongest evaluated baseline on the corresponding task. Comparisons with individual policies further show that the benefit of force feedback depends on action bandwidth, supporting its selective use with fine motion prediction.