Contact-rich ManipulationIntermediate
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
RATE (preprint) introduces a risk-aware tactile encoding method that learns task-conditioned contact risk from tactile history and alert supervision, then injects it into a frozen ACT policy through a lightweight residual adapter. It reports 71.8% average success on eight UniVTAC simulation tasks and 73.3% average success on three real-world tasks, outperforming baselines including 3.3B-5B parameter visuomotor policies while updating only about 0.59% of model parameters. This matters for contact-rich manipulation because similar tactile readings can hide very different failure risks.