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Found 12 papers, 0 papers with code
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Generalization remains one of the most important desiderata for robust robot learning systems.
Learning Sequential Acquisition Policies for Robot-Assisted Feeding
A robot providing mealtime assistance must perform specialized maneuvers with various utensils in order to pick up and feed a range of food items.
KITE: Keypoint-Conditioned Policies for Semantic Manipulation
While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in manipulation.
Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding
Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across food groups.
DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects
However, for highly deformable objects it is challenging to align the output of a simulator with the behavior of real objects.
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Deformable object manipulation remains a challenging task in robotics research.
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.
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.
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
We explore learning pixelwise correspondences between images of deformable objects in different configurations.
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.
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation.
