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Found 9 papers, 2 papers with code
Hysteresis Compensation of Flexible Continuum Manipulator using RGBD Sensing and Temporal Convolutional Network
This result implies that the proposed calibrated controller effectively reaches the desired configurations by estimating the hysteresis of the manipulator.
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which operate alongside HULK, a high-level planner from prior work.
Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans
Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS).
Robotics
Untangling Dense Knots by Learning Task-Relevant Keypoints
HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97. 9% of 378 simulation experiments with an average of 12. 1 actions per trial.
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics.
Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks
Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis.
Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot
We report experimental results for a handover-free version of the peg transfer task, performing 20 and 5 physical episodes with single- and bilateral-arm setups, respectively.
Robotics
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone.
