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Found 14 papers, 6 papers with code
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
We apply our system to perform articulated object manipulation, both in the simulation and the real world.
Learning to Regrasp by Learning to Place
In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses.
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably.
GRAC: Self-Guided and Self-Regularized Actor-Critic
One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function.
Generative 3D Part Assembly via Dynamic Graph Learning
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
Design and Control of Roller Grasper V2 for In-Hand Manipulation
The ability to perform in-hand manipulation still remains an unsolved problem; having this capability would allow robots to perform sophisticated tasks requiring repositioning and reorienting of grasped objects.
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
Learning to Scaffold the Development of Robotic Manipulation Skills
To combat these factors and achieve more robust manipulation, humans actively exploit contact constraints in the environment.
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
ClusterNet: 3D Instance Segmentation in RGB-D Images
We show that our method generalizes well on real-world data achieving visually better segmentation results.
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
Our model jointly estimates (i) the segmentation of the scene into an unknown but finite number of objects, (ii) the motion trajectories of these objects and (iii) the object scene flow.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
