Efficient and Interpretable Robot Manipulation with Graph Neural Networks

• URL: http://arxiv.org/abs/2102.13177v1
• Date: Thu, 25 Feb 2021 21:09:12 GMT
• Title: Efficient and Interpretable Robot Manipulation with Graph Neural Networks
• Authors: Yixin Lin, Austin S. Wang, Akshara Rai
• Abstract summary: We represent manipulation tasks as operations over graphs, using graph neural networks (GNNs) Our formulation first transforms the environment into a graph representation, then applies a trained GNN policy to predict which object to manipulate towards which goal state. Our GNN policies are trained using very few expert demonstrations on simple tasks, and exhibits generalization over number and configurations of objects in the environment. We present experiments which show that a single learned GNN policy can solve a variety of blockstacking tasks in both simulation and real hardware.
• Score: 7.799182201815763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many manipulation tasks can be naturally cast as a sequence of spatial relationships and constraints between objects. We aim to discover and scale these task-specific spatial relationships by representing manipulation tasks as operations over graphs. To do this, we pose manipulating a large, variable number of objects as a probabilistic classification problem over actions, objects and goals, learned using graph neural networks (GNNs). Our formulation first transforms the environment into a graph representation, then applies a trained GNN policy to predict which object to manipulate towards which goal state. Our GNN policies are trained using very few expert demonstrations on simple tasks, and exhibits generalization over number and configurations of objects in the environment and even to new, more complex tasks, and provide interpretable explanations for their decision-making. We present experiments which show that a single learned GNN policy can solve a variety of blockstacking tasks in both simulation and real hardware.

Related papers

• MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies [25.512068008948603]
  MATCH POLICY is a pipeline for solving high-precision pick and place tasks. It transfers action inference into a point cloud registration task. It achieves extremely high sample efficiency and generalizability to unseen configurations.
  arXiv  Detail & Related papers  (2024-09-23T20:09:43Z)
• Can Graph Learning Improve Planning in LLM-based Agents? [61.47027387839096]
  Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs) In this paper, we explore graph learning-based methods for task planning, a direction that is to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs.
  arXiv  Detail & Related papers  (2024-05-29T14:26:24Z)
• Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging [1.807492010338763]
  Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus on designing an expert system for each specific task by model-based or data-driven approaches. We design a local GNN (Graph Neural Network) based learning method, which utilizes two representation graphs to encode keypoints detected from images. Our framework is effective in multiple 1-D (rope, rope ring) and 2-D (cloth) rearranging tasks in simulation and can be easily transferred to a real robot by fine-tuning a keypoint detector
  arXiv  Detail & Related papers  (2023-02-21T05:21:26Z)
• Visual Transformer for Object Detection [0.0]
  We consider the use of self-attention for discriminative visual tasks, object detection, as an alternative to convolutions. Our model leads to consistent improvements in object detection on COCO across many different models and scales.
  arXiv  Detail & Related papers  (2022-06-01T06:13:09Z)
• Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach [5.629161809575013]
  We propose a novel training strategy for graph representation learning, based on meta-learning. Our method avoids the difficulties arising when learning to perform multiple tasks concurrently. We show that the embeddings produced by a model trained with our method can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.
  arXiv  Detail & Related papers  (2022-01-10T12:58:46Z)
• SORNet: Spatial Object-Centric Representations for Sequential Manipulation [39.88239245446054]
  Sequential manipulation tasks require a robot to perceive the state of an environment and plan a sequence of actions leading to a desired goal state. We propose SORNet, which extracts object-centric representations from RGB images conditioned on canonical views of the objects of interest.
  arXiv  Detail & Related papers  (2021-09-08T19:36:29Z)
• RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks [53.15260967235835]
  We propose a novel framework that refines the output of such methods by utilizing a graph-based representation of instance masks. We train deep networks capable of sampling smart perturbations to the segmentations, and a graph neural network, which can encode relations between objects, to evaluate the segmentations. We demonstrate an application that uses uncertainty estimates generated by our method to guide a manipulator, leading to efficient understanding of cluttered scenes.
  arXiv  Detail & Related papers  (2021-06-29T20:29:29Z)
• Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients [122.85280150421175]
  We present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. We employ a graph neural network (GNN) to parameterize policies for the robots.
  arXiv  Detail & Related papers  (2021-02-11T21:57:43Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)
• Evaluating Logical Generalization in Graph Neural Networks [59.70452462833374]
  We study the task of logical generalization using graph neural networks (GNNs) Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics. We find that the ability for models to generalize and adapt is strongly determined by the diversity of the logical rules they encounter during training.
  arXiv  Detail & Related papers  (2020-03-14T05:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.