Contact SLAM: An Active Tactile Exploration Policy Based on Physical Reasoning Utilized in Robotic Fine Blind Manipulation Tasks
Contact-rich manipulation is difficult for robots to execute and requires accurate perception of the environment. In some scenarios, vision is occluded. The robot can then no longer obtain real-time scene state information through visual feedback. This is called `blind manipulation". In this manuscript, a novel physically-driven contact cognition method, called `Contact SLAM", is proposed. It estimates the state of the environment and achieves manipulation using only tactile sensing and prior knowledge of the scene. To maximize exploration efficiency, this manuscript also designs an active exploration policy. The policy gradually reduces uncertainties in the manipulation scene. The experimental results demonstrated the effectiveness and accuracy of the proposed method in several contact-rich tasks, including the difficult and delicate socket assembly task and block-pushing task.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Bayesian Active Object Recognition and 6D Pose Estimation from Multimodal Contact Sensing
We present an active tactile exploration framework for joint object recognition and 6D pose estimation. The proposed method integrates wrist force/torque sensing, GelSight tactile sensing, and free-space constraints with…
Object RecognitionBayesian Inference6D Pose EstimationMotion PlanningReactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation
Humans can accomplish complex contact-rich tasks using vision and touch, with highly reactive capabilities such as quick adjustments to environmental changes and adaptive control of contact forces; however, this remains …
Contact-rich ManipulationImitation LearningGelSLAM: A Real-time, High-Fidelity, and Robust 3D Tactile SLAM System
Accurately perceiving an object's pose and shape is essential for precise grasping and manipulation. Compared to common vision-based methods, tactile sensing offers advantages in precision and immunity to occlusion when …
TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks. We present …
Reinforcement LearningRobot ManipulationActive Tactile Exploration for Rigid Body Pose and Shape Estimation
General robot manipulation requires the handling of previously unseen objects. Learning a physically accurate model at test time can provide significant benefits in data efficiency, predictability, and reuse between task…
Robot Manipulation