BayesContact: Simulation-Based Inference for Visuo-Tactile Pose Estimation in Peg-in-Hole Insertion
BayesContact is a Simulation-Based Inference framework that combines depth and force/torque observations to estimate object pose during peg-in-hole insertion tasks. The method maintains a particle belief over pose, using a renderer and physics simulator to score hypotheses against real observations. In both simulated and real-robot experiments, BayesContact improves pose observability and increases insertion success by 30% compared to vision-only inference.
Why it matters: This work offers a practical approach to fusing vision and tactile sensing for precise pose estimation in contact-rich robotic manipulation, potentially reducing the need for retraining across different environments and geometries.
Full story at: arXiv Robotics ↗