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Found 13 papers, 3 papers with code
Diff-DOPE: Differentiable Deep Object Pose Estimation
We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a 3D textured model of an object, and an initial pose of the object.
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Our policies are trained to adapt to a wide range of conditions in simulation.
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world.
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, planning a collision-free trajectory, and then executing the open-loop trajectory to the grasp pose with a low-level tracking controller.
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.
Correcting Robot Plans with Natural Language Feedback
In this paper, we explore natural language as an expressive and flexible tool for robot correction.
Model Predictive Control for Fluid Human-to-Robot Handovers
However, how to responsively generate smooth motions to take an object from a human is still an open question.
Learning Perceptual Concepts by Bootstrapping from Human Queries
Second, we treat this low-dimensional concept as an automatic labeler to synthesize a large-scale high-dimensional data set with the simulator.
In-Hand Object-Dynamics Inference using Tactile Fingertips
We show that tactile fingertips enable in-hand dynamics estimation of low mass objects.
Robotics
Multi-Fingered Grasp Planning via Inference in Deep Neural Networks
We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success.
Robotics
Benchmarking In-Hand Manipulation
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems.
Robotics
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization.
Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data.
Robotics
