We are announcing the release of our state-of-the-art off-policy model-free
reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has
been developed jointly at UC Berkeley and Google, and we have been using
it internally for our robotics experiment. Soft actor-critic is, to our
knowledge, one of the most efficient model-free algorithms available today,
making it especially well-suited for real-world robotic learning. In this post,
we will benchmark SAC against state-of-the-art model-free RL algorithms and
showcase a spectrum of real-world robot examples, ranging from manipulation to
locomotion. We also release our implementation of SAC, which is particularly
designed for real-world robotic systems.
We just rolled out general support for multi-agent reinforcement learning in
Ray RLlib 0.6.0. This blog post is a brief tutorial on multi-agent RL and
how we designed for it in RLlib. Our goal is to enable multi-agent RL across a
range of use cases, from leveraging existing single-agent algorithms to training
with custom algorithms at large scale.
Figure: An artistic representation of single-cell RNA sequencing. The
stars in the sky represent cells in a heterogeneous tissue. The projection of
the stars onto the river reveals relationships among them that are not apparent
by looking directly at the sky. Like the river, our Bayesian model, called scVI,
reveals relationships among cells.
The diversity of gene regulatory states in our body is one of the main reasons
why such an amazing array of biological functions can be encoded in a single
genome. Recent advances in microfluidics and sequencing technologies (such as
inDrops) enabled measurement of gene expression at the single-cell level and has
provided tremendous opportunities to unravel the underlying mechanisms of
relationships between individual genes and specific biological phenomena. These
experiments yield approximate measurements for mRNA counts of the entire
transcriptome (i.e around $d = 20,000$ protein-coding genes) and a large number
of cells $n$, which can vary from tens of thousands to a million cells. The
early computational methods to interpret this data relied on linear model and
empirical Bayes shrinkage approaches due to initially extremely low sample-size.
While current research focuses on providing more accurate models for this gene
expression data, most of the subsequent algorithms either exhibit prohibitive
scalability issues or remain limited to a unique downstream analysis task.
Consequently, common practices in the field still rely on ad-hoc preprocessing
pipelines and specific algorithmic procedures, which limits the capabilities of
capturing the underlying data generating process.
In this post, we propose to build up on the increased sample-size and recent
developments in Bayesian approximate inference to improve modeling complexity as
well as algorithmic scalability. Notably, we present our recent work on deep
generative models for single-cell transcriptomics, which addresses all the
mentioned limitations by formalizing biological questions into statistical
queries over a unique graphical model, tailored to single-cell RNA sequencing
(scRNA-seq) datasets. The resulting algorithmic inference procedure, which we
named Single-cell Variational Inference (scVI), is open-source and
scales to over a million cells.
With very little explicit supervision and feedback, humans are able to learn a
wide range of motor skills by simply interacting with and observing the world
through their senses. While there has been significant progress towards building
machines that can learn complex
skills and learn
based on raw sensory information such as image pixels, acquiring large and
diverse repertoires of general skills remains an open challenge. Our goal is
to build a generalist: a robot that can perform many different tasks, like
arranging objects, picking up toys, and folding towels, and can do so with many
different objects in the real world without re-learning for each object or task.
While these basic motor skills are much simpler and less impressive than mastering Chess or even using a spatula, we think that
being able to achieve such generality with a single model is a fundamental
aspect of intelligence.
The key to acquiring generality is diversity. If you deploy a learning
algorithm in a narrow, closed-world environment, the agent will recover skills
that are successful only in a narrow range of settings. That’s why an algorithm
trained to play Breakout will struggle when anything about the images or the
game changes. Indeed, the success of image classifiers relies on large, diverse
datasets like ImageNet. However, having a robot autonomously learn from large
and diverse datasets is quite challenging. While collecting diverse sensory data
is relatively straightforward, it is simply not practical for a person to
annotate all of the robot’s experiences. It is more scalable to collect
completely unlabeled experiences. Then, given only sensory data, akin to what
humans have, what can you learn? With raw sensory data there is no notion of
progress, reward, or success. Unlike games like Breakout, the real world doesn’t
give us a score or extra lives.
We have developed an algorithm that can learn a general-purpose predictive model
using unlabeled sensory experiences, and then use this single model to perform a
wide range of tasks.
With a single model, our approach can perform a wide range of tasks, including
lifting objects, folding shorts, placing an apple onto a plate, rearranging
objects, and covering a fork with a towel.
In this post, we will describe how this works. We will discuss how we can learn
based on only raw sensory interaction data (i.e. image pixels, without requiring
object detectors or hand-engineered perception components). We will show how we
can use what was learned to accomplish many different user-specified tasks. And,
we will demonstrate how this approach can control a real robot from raw pixels,
performing tasks and interacting with objects that the robot has never seen
Figure 1: (left) LED Array Microscope constructed using a standard
commercial microscope and an LED array. (middle) Close up on the LED array dome
mounted on the microscope. (right) LED array displaying patterns at 100Hz.
Computational imaging systems marry the design of hardware and image
reconstruction. For example, in optical microscopy, tomographic,
super-resolution, and phase imaging systems can be constructed from
simple hardware modifications to a commercial microscope (Fig. 1) and
computational reconstruction. Traditionally, we require a large number of
measurements to recover the above quantities; however, for live cell imaging
applications, we are limited in the number of measurements we can acquire due
to motion. Naturally, we want to know what are the best measurements to acquire.
In this post, we highlight our latest work that learns the experimental design to maximize the performance of a
non-linear computational imaging system.
In many tasks in machine learning, it is common to want to answer questions
given fixed, pre-collected datasets. In some applications, however, we are not
given data a priori; instead, we must collect the data we require to answer the
questions of interest. This situation arises, for example, in environmental
contaminant monitoring and census-style surveys. Collecting the data ourselves
allows us to focus our attention on just the most relevant sources of
information. However, determining which of these sources of information will
yield useful measurements can be difficult. Furthermore, when data is collected
by a physical agent (e.g. robot, satellite, human, etc.) we must plan our
measurements so as to reduce costs associated with the motion of the agent over
time. We call this abstract problem embodied adaptive sensing.
We introduce a new approach to the embodied adaptive sensing problem, in which a
robot must traverse its environment to identify locations or items of interest.
Adaptive sensing encompasses many well-studied problems in robotics, including
the rapid identification of accidental contamination leaks and radioactive
sources, and finding individuals in search and rescue missions. In such
settings, it is often critical to devise a sensing trajectory that returns a
correct solution as quickly as possible.
Top left: image of a 3D cube. Top right: example depth image, with darker points
representing areas closer to the camera (source: Wikipedia). Next two
rows: examples of depth and RGB image pairs for grasping objects in a bin. Last
two rows: similar examples for bed-making.
For robot perception, convolutional neural networks (CNNs), such as
VGG or ResNet, with three RGB color channels have become standard. For
robotics and computer vision tasks, it is common to borrow one of these
architectures (along with pre-trained weights) and then to perform transfer
learning or fine-tuning on task-specific data. But in some tasks, knowing
the colors in an image may provide only limited benefits. Consider training a
robot to grasp novel, previously unseen objects. It may be more important to
understand the geometry of the environment rather than colors and textures. The
physical process of manipulation — controlling one or more objects by applying
forces through contact — depends on object geometry, pose, and other factors
which are largely color-invariant. When you manipulate a pen with your hand, for
instance, you can often move it seamlessly without looking at the actual pen, so
long as you have a good understanding of the location and orientation of contact
points. Thus, before proceeding, one might ask: does it makes sense to use
There is an alternative: depth images. These are single-channel grayscale
images that measure depth values from the camera, and give us invariance to the
colors of objects within an image. We can also use depth to “filter” points
beyond a certain distance which can remove background noise, as we demonstrate
later with robot bed-making. Examples of paired depth and real images are shown
In this post, we consider the potential for combining depth images and deep
learning in the context of three ongoing projects in the UC Berkeley
AUTOLab: Dex-Net for robot grasping, segmenting objects in heaps, and robot
Simulated characters imitating skills from YouTube videos.
Whether it’s everyday tasks like washing our hands or stunning feats of
acrobatic prowess, humans are able to learn an incredible array of skills by
watching other humans. With the proliferation of publicly available video data
from sources like YouTube, it is now easier than ever to find video clips of
whatever skills we are interested in. A staggering 300 hours of videos are
uploaded to YouTube every minute. Unfortunately, it is still very challenging
for our machines to learn skills from this vast volume of visual data. Most
imitation learning approaches require concise representations, such as those
recorded from motion capture (mocap). But getting mocap data can be quite a
hassle, often requiring heavy instrumentation. Mocap systems also tend to be
restricted to indoor environments with minimal occlusion, which can limit the
types of skills that can be recorded. So wouldn’t it be nice if our agents can
also learn skills by watching video clips?
In this work, we present a framework for learning skills from videos (SFV). By
combining state-of-the-art techniques in computer vision and reinforcement
learning, our system enables simulated characters to learn a diverse
repertoire of skills from video clips. Given a single monocular video of an
actor performing some skill, such as a cartwheel or a backflip, our characters
are able to learn policies that reproduce that skill in a physics simulation,
without requiring any manual pose annotations.
We want to build agents that can accomplish arbitrary goals in unstructured
complex environments, such as a personal robot that can perform household
chores. A promising approach is to use deep reinforcement learning, which is a
powerful framework for teaching agents to maximize a reward function. However,
the typical reinforcement learning paradigm involves training an agent to solve
an individual task with a manually designed reward. For example, you might train
a robot to set a dinner table by designing a reward function based on the
distance between each plate or utensil and its goal location. This setup
requires a person to design the reward function for each task, as well as extra
systems like object detectors, which can be expensive and brittle. Moreover, if
we want machines that can perform a large repertoire of chores, we would have to
repeat this RL training procedure on each new task.
While designing reward functions and setting up sensors
(door angle measurement, object detectors, etc.) may be
easy in simulation, it quickly becomes impractical in
the real world (right image).
We train agents to solve various tasks from
vision without extra instrumentation. The top row shows goal images and the
bottom row shows our policies reaching those goals.
In this post, we discuss reinforcement learning algorithms that can be used to
learn multiple different tasks simultaneously, without additional human
supervision. For an agent to acquire skills without human intervention, it must
be able to set goals for itself, interact with the environment, and evaluate
whether it has achieved its goals to improve its behavior, all from raw
observations such as images without manually engineering extra components like
object detectors. We introduce a system that sets abstract goals and
autonomously learns to achieve those goals. We then show that we can use these
autonomously learned skills to perform a variety of user-specified goals, such
as pushing objects, grasping objects, and opening doors, without any additional
learning. Lastly, we demonstrate that our method is efficient enough to work in
the real world on a Sawyer robot. The robot learns to set and achieve goals
involving pushing an object to a specific location, with only images as the
input to the system.
In this post, we demonstrate how deep reinforcement learning (deep RL) can be
used to learn how to control dexterous hands for a variety of manipulation
tasks. We discuss how such methods can learn to make use of low-cost hardware,
can be implemented efficiently, and how they can be complemented with techniques
such as demonstrations and simulation to accelerate learning.