15 research outputs found
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
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
Swoosh! Rattle! Thump! -- Actions that Sound
Truly intelligent agents need to capture the interplay of all their senses to
build a rich physical understanding of their world. In robotics, we have seen
tremendous progress in using visual and tactile perception; however, we have
often ignored a key sense: sound. This is primarily due to the lack of data
that captures the interplay of action and sound. In this work, we perform the
first large-scale study of the interactions between sound and robotic action.
To do this, we create the largest available sound-action-vision dataset with
15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By
tilting objects and allowing them to crash into the walls of a robotic tray, we
collect rich four-channel audio information. Using this data, we explore the
synergies between sound and action and present three key insights. First, sound
is indicative of fine-grained object class information, e.g., sound can
differentiate a metal screwdriver from a metal wrench. Second, sound also
contains information about the causal effects of an action, i.e. given the
sound produced, we can predict what action was applied to the object. Finally,
object representations derived from audio embeddings are indicative of implicit
physical properties. We demonstrate that on previously unseen objects, audio
embeddings generated through interactions can predict forward models 24% better
than passive visual embeddings. Project videos and data are at
https://dhiraj100892.github.io/swoosh/Comment: To be presented at Robotics: Science and Systems 202