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Found 19 papers, 4 papers with code
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
In physical experiments, we find a 13. 7% increase in success rate, a 1. 6x increase in picks per hour, and a 6. 3x decrease in grasp planning time compared to prior work on multi-object grasping.
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception
Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling.
Autonomously Untangling Long Cables
Cables are ubiquitous in many settings and it is often useful to untangle them.
Multi-Object Grasping in the Plane
In physical grasping experiments comparing performance with a single-object picking baseline, we find that the frictionless multi-object grasping system achieves 13. 6\% higher grasp success and is 59. 9\% faster, from 212 PPH to 340 PPH.
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models
For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards.
Mechanical Search on Shelves using a Novel "Bluction" Tool
Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency.
Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects
The ability to grasp and manipulate transparent objects is a major challenge for robots.
Accelerating Quadratic Optimization with Reinforcement Learning
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly.
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.
Disentangling Dense Multi-Cable Knots
Disentangling two or more cables requires many steps to remove crossings between and within cables.
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images.
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.
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations.
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.
GOMP: Grasp-Optimized Motion Planning for Bin Picking
Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH).
Robotics
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.
