234 research outputs found
A Single Multi-Task Deep Neural Network with a Multi-Scale Feature Aggregation Mechanism for Manipulation Relationship Reasoning in Robotic Grasping
Grasping specific objects in complex and irregularly stacked scenes is still
challenging for robotics. Because the robot is not only required to identify
the object's grasping posture but also needs to reason the manipulation
relationship between the objects. In this paper, we propose a manipulation
relationship reasoning network with a multi-scale feature aggregation (MSFA)
mechanism for robot grasping tasks. MSFA aggregates high-level semantic
information and low-level spatial information in a cross-scale connection way
to improve the generalization ability of the model. Furthermore, to improve the
accuracy, we propose to use intersection features with rich location priors for
manipulation relationship reasoning. Experiments are validated in VMRD datasets
and real environments, respectively. The experimental results demonstrate that
our proposed method can accurately predict the manipulation relationship
between objects in the scene of multi-object stacking. Compared with previous
methods, it significantly improves reasoning speed and accuracy
Learning Multi-step Robotic Manipulation Tasks through Visual Planning
Multi-step manipulation tasks in unstructured environments are extremely challenging for a robot to learn. Such tasks interlace high-level reasoning that consists of the expected states that can be attained to achieve an overall task and low-level reasoning that decides what actions will yield these states. A model-free deep reinforcement learning method is proposed to learn multi-step manipulation tasks. This work introduces a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel image input at real-time speeds (20ms). The proposed model architecture achieved a state-of-the-art accuracy on three standard grasping datasets. The adaptability of the proposed approach is demonstrated by directly transferring the trained model to a 7 DoF robotic manipulator with a grasp success rate of 95.4% and 93.0% on novel household and adversarial objects, respectively. A novel Robotic Manipulation Network (RoManNet) is introduced, which is a vision-based model architecture, to learn the action-value functions and predict manipulation action candidates. A Task Progress based Gaussian (TPG) reward function is defined to compute the reward based on actions that lead to successful motion primitives and progress towards the overall task goal. To balance the ratio of exploration/exploitation, this research introduces a Loss Adjusted Exploration (LAE) policy that determines actions from the action candidates according to the Boltzmann distribution of loss estimates. The effectiveness of the proposed approach is demonstrated by training RoManNet to learn several challenging multi-step robotic manipulation tasks in both simulation and real-world. Experimental results show that the proposed method outperforms the existing methods and achieves state-of-the-art performance in terms of success rate and action efficiency. The ablation studies show that TPG and LAE are especially beneficial for tasks like multiple block stacking
Domain Randomization and Generative Models for Robotic Grasping
Deep learning-based robotic grasping has made significant progress thanks to
algorithmic improvements and increased data availability. However,
state-of-the-art models are often trained on as few as hundreds or thousands of
unique object instances, and as a result generalization can be a challenge.
In this work, we explore a novel data generation pipeline for training a deep
neural network to perform grasp planning that applies the idea of domain
randomization to object synthesis. We generate millions of unique, unrealistic
procedurally generated objects, and train a deep neural network to perform
grasp planning on these objects.
Since the distribution of successful grasps for a given object can be highly
multimodal, we propose an autoregressive grasp planning model that maps sensor
inputs of a scene to a probability distribution over possible grasps. This
model allows us to sample grasps efficiently at test time (or avoid sampling
entirely).
We evaluate our model architecture and data generation pipeline in simulation
and the real world. We find we can achieve a 90% success rate on previously
unseen realistic objects at test time in simulation despite having only been
trained on random objects. We also demonstrate an 80% success rate on
real-world grasp attempts despite having only been trained on random simulated
objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2018
Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images
While traditional methods relies on depth sensors, the current trend leans
towards utilizing cost-effective RGB images, despite their absence of depth
cues. This paper introduces an interesting approach to detect grasping pose
from a single RGB image. To this end, we propose a modular learning network
augmented with grasp detection and semantic segmentation, tailored for robots
equipped with parallel-plate grippers. Our network not only identifies
graspable objects but also fuses prior grasp analyses with semantic
segmentation, thereby boosting grasp detection precision. Significantly, our
design exhibits resilience, adeptly handling blurred and noisy visuals. Key
contributions encompass a trainable network for grasp detection from RGB
images, a modular design facilitating feasible grasp implementation, and an
architecture robust against common image distortions. We demonstrate the
feasibility and accuracy of our proposed approach through practical experiments
and evaluations
Instance-wise Grasp Synthesis for Robotic Grasping
Generating high-quality instance-wise grasp configurations provides critical
information of how to grasp specific objects in a multi-object environment and
is of high importance for robot manipulation tasks. This work proposed a novel
\textbf{S}ingle-\textbf{S}tage \textbf{G}rasp (SSG) synthesis network, which
performs high-quality instance-wise grasp synthesis in a single stage: instance
mask and grasp configurations are generated for each object simultaneously. Our
method outperforms state-of-the-art on robotic grasp prediction based on the
OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The
benchmarking results showed significant improvements compared to the baseline
on the accuracy of generated grasp configurations. The performance of the
proposed method has been validated through both extensive simulations and real
robot experiments for three tasks including single object pick-and-place, grasp
synthesis in cluttered environments and table cleaning task
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