1,307 research outputs found
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
CASSL: Curriculum Accelerated Self-Supervised Learning
Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We present a novel approach - Curriculum Accelerated Self-Supervised
Learning (CASSL) - to train policies that map visual information to high-level,
higher- dimensional action spaces. CASSL orders the sampling of training data
based on control dimensions: the learning and sampling are focused on few
control parameters before other parameters. The right curriculum for learning
is suggested by variance-based global sensitivity analysis of the control
space. We apply our CASSL framework to learning how to grasp using an adaptive,
underactuated multi-fingered gripper, a challenging system to control. Our
experimental results indicate that CASSL provides significant improvement and
generalization compared to baseline methods such as staged curriculum learning
(8% increase) and complete end-to-end learning with random exploration (14%
improvement) tested on a set of novel objects
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