Toyota HSR Trained with DART to Make a Bed.
In Imitation Learning (IL), also known as Learning from Demonstration (LfD), a
robot learns a control policy from analyzing demonstrations of the policy
performed by an algorithmic or human supervisor. For example, to teach a robot
make a bed, a human would tele-operate a robot to perform the task to provide
examples. The robot then learns a control policy, mapping from images/states to
actions which we hope will generalize to states that were not encountered during
There are two variants of IL: Off-Policy, or Behavior Cloning, where the
demonstrations are given independent of the robot’s policy. However, when the
robot encounters novel risky states it may not have learned corrective actions.
This occurs because of “covariate shift” a known challenge, where the states
encountered during training differ from the states encountered during testing,
reducing robustness. Common approaches to reduce covariate shift are On-Policy
methods, such as DAgger, where the evolving robot’s policy is executed and the
supervisor provides corrective feedback. However, On-Policy methods can be
difficult for human supervisors, potentially dangerous, and computationally
This post presents a robust Off-Policy algorithm called DART and summarizes how
injecting noise into the supervisor’s actions can improve robustness. The
injected noise allows the supervisor to provide corrective examples for the type
of errors the trained robot is likely to make. However, because the optimized
noise is small, it alleviates the difficulties of On-Policy methods. Details on
DART are in a paper that will be presented at the 1st Conference on Robot Learning in
We evaluate DART in simulation with an algorithmic supervisor on MuJoCo tasks
(Walker, Humanoid, Hopper, Half-Cheetah) and physical experiments with human
supervisors training a Toyota HSR robot to perform grasping in clutter, where a
robot must search through clutter for a goal object. Finally, we show how
DART can be applied in a complex system that leverages both classical robotics
and learning techniques to teach the first robot to make a bed. For
researchers who want to study and use robust Off-Policy approaches, we
additionally announce the release of
Deep imitation learning and deep reinforcement learning have potential to learn
robot control policies that map high-dimensional sensor inputs to controls.
While these approaches have been very successful at learning short duration tasks, such
as grasping (Pinto and Gupta 2016, Levine et al. 2016) and peg insertion (Levine
et al. 2016), scaling learning to longer time horizons can require a prohibitive
amount of demonstration data—whether acquired from experts or self-supervised.
Long-duration sequential tasks suffer from the classic problem of “temporal
credit assignment”, namely, the difficulty in assigning credit (or blame) to
actions under uncertainty of the time when their consequences are observed
(Sutton 1984). However, long-term behaviors are often composed of short-term
skills that solve decoupled subtasks. Consider designing a controller for
parallel parking where the overall task can be decomposed into three phases:
pulling up, reversing, and adjusting. Similarly, assembly tasks can often be
decomposed into individual steps based on which parts need to be manipulated.
These short-term skills can be parametrized more concisely—as an analogy,
consider locally linear approximations to an overall nonlinear function—and
this reduced parametrization can be substantially easier to learn.
This post summarizes results from three recent papers that propose algorithms
that learn to decompose a longer task into shorter subtasks. We report
experiments in the context of autonomous surgical subtasks and we believe the
results apply to a variety of applications from manufacturing to home robotics.
We present three algorithms: Transition State Clustering (TSC), Sequential
Windowed Inverse Reinforcement Learning (SWIRL), and Deep Discovery of
Continuous Options (DDCO). TSC considers robustly learning important switching
events (significant changes in motion) that occur across all demonstrations.
SWIRL proposes an algorithm that approximates a value function by a sequence of
shorter term quadratic rewards. DDCO is a general framework for imitation
learning with a hierarchical representation of the action space. In retrospect,
all three algorithms are special cases of the same general framework, where the
demonstrator’s behavior is generatively modeled as a sequential composition of
unknown closed-loop policies that switch when reaching parameterized “transition
Deep reinforcement learning (deep RL) has achieved success in many tasks, such as playing video games from raw pixels (Mnih et al., 2015), playing the game of Go (Silver et al., 2016), and simulated robotic locomotion (e.g. Schulman et al., 2015). Standard deep RL algorithms aim to master a single way to solve a given task, typically the first way that seems to work well. Therefore, training is sensitive to randomness in the environment, initialization of the policy, and the algorithm implementation. This phenomenon is illustrated in Figure 1, which shows two policies trained to optimize a reward function that encourages forward motion: while both policies have converged to a high-performing gait, these gaits are substantially different from each other.
Figure 1: Trained simulated walking robots.
[credit: John Schulman and Patrick Coady (OpenAI Gym)]
Why might finding only a single solution be undesirable? Knowing only one way to act makes agents vulnerable to environmental changes that are common in the real-world. For example, consider a robot (Figure 2) navigating its way to the goal (blue cross) in a simple maze. At training time (Figure 2a), there are two passages that lead to the goal. The agent will likely commit to the solution via the upper passage as it is slightly shorter. However, if we change the environment by blocking the upper passage with a wall (Figure 2b), the solution the agent has found becomes infeasible. Since the agent focused entirely on the upper passage during learning, it has almost no knowledge of the lower passage. Therefore, adapting to the new situation in Figure 2b requires the agent to relearn the entire task from scratch.
Figure 2: A robot navigating a maze.
Since we posted our paper on “Learning to Optimize” last year, the area of optimizer learning has received growing attention. In this article, we provide an introduction to this line of work and share our perspective on the opportunities and challenges in this area.
Machine learning has enjoyed tremendous success and is being applied to a wide variety of areas, both in AI and beyond. This success can be attributed to the data-driven philosophy that underpins machine learning, which favours automatic discovery of patterns from data over manual design of systems using expert knowledge.
Yet, there is a paradox in the current paradigm: the algorithms that power machine learning are still designed manually. This raises a natural question: can we learn these algorithms instead? This could open up exciting possibilities: we could find new algorithms that perform better than manually designed algorithms, which could in turn improve learning capability.
Consider looking at a photograph of a chair.
We humans have the remarkable capacity of inferring properties about the 3D shape of the chair from this single photograph even if we might not have seen such a chair ever before.
A more representative example of our experience though is being in the same physical space as the chair and accumulating information from various viewpoints around it to build up our hypothesis of the chair’s 3D shape.
How do we solve this complex 2D to 3D inference task? What kind of cues do we use?
How do we seamlessly integrate information from just a few views to build up a holistic 3D model of the scene?
A vast body of work in computer vision has been devoted to developing algorithms which leverage various cues from images that enable this task of 3D reconstruction.
They range from monocular cues such as shading, linear perspective, size constancy etc. to binocular and even multi-view stereopsis.
The dominant paradigm for integrating multiple views has been to leverage stereopsis, i.e. if a point in the 3D world is viewed from multiple viewpoints, its location in 3D can be determined by triangulating its projections in the respective views.
This family of algorithms has led to work on Structure from Motion (SfM) and Multi-view Stereo (MVS) and have been used to produce city-scale 3D models and enable rich visual experiences such as 3D flyover maps.
With the advent of deep neural networks and their immense power in modelling visual data, the focus has recently shifted to modelling monocular cues implicitly with a CNN and predicting 3D from a single image as depth/surface orientation maps or 3D voxel grids.
In our recent work, we tried to unify these paradigms of single and multi-view 3D reconstruction.
We proposed a novel system called a Learnt Stereo Machine (LSM) that can leverage monocular/semantic cues for single-view 3D reconstruction while also being able to integrate information from multiple viewpoints using stereopsis - all within a single end-to-end learnt deep neural network.
This post was initially published on Off the Convex Path. It is reposted here with authors’ permission.
A core, emerging problem in nonconvex optimization involves the escape of saddle points. While recent research has shown that gradient descent (GD) generically escapes saddle points asymptotically (see Rong Ge’s and Ben Recht’s blog posts), the critical open problem is one of efficiency — is GD able to move past saddle points quickly, or can it be slowed down significantly? How does the rate of escape scale with the ambient dimensionality? In this post, we describe our recent work with Rong Ge, Praneeth Netrapalli and Sham Kakade, that provides the first provable positive answer to the efficiency question, showing that, rather surprisingly, GD augmented with suitable perturbations escapes saddle points efficiently; indeed, in terms of rate and dimension dependence it is almost as if the saddle points aren’t there!
Digitally reconstructing 3D geometry from images is a core problem in computer vision. There are various applications, such as movie productions, content generation for video games, virtual and augmented reality, 3D printing and many more. The task discussed in this blog post is reconstructing high quality 3D geometry from a single color image of an object as shown in the figure below.
Humans have the ability to effortlessly reason about the shapes of objects and scenes even if we only see a single image. Note that the binocular arrangement of our eyes allows us to perceive depth, but it is not required to understand 3D geometry. Even if we only see a photograph of an object we have a good understanding of its shape. Moreover, we are also able to reason about the unseen parts of objects such as the back, which is an important ability for grasping objects. The question which immediately arises is how are humans able to reason about geometry from a single image? And in terms of artificial intelligence: how can we teach machines this ability?
Be careful what you reward
“Be careful what you wish for!” – we’ve all heard it! The story of King Midas
is there to warn us of what might happen when we’re not. Midas, a king who loves
gold, runs into a satyr and wishes that everything he touches would turn to gold.
Initially, this is fun and he walks around turning items to gold. But his
happiness is short lived. Midas realizes the downsides of his wish when he hugs
his daughter and she turns into a golden statue.
We, humans, have a notoriously difficult time specifying what we actually want,
and the AI systems we build suffer from it. With AI, this warning actually
becomes “Be careful what you reward!”. When we design and deploy an AI agent
for some application, we need to specify what we want it to do, and this
typically takes the form of a reward function: a function that tells the agent
which state and action combinations are good. A car reaching its destination is
good, and a car crashing into another car is not so good.
AI research has made a lot of progress on algorithms for generating AI behavior
that performs well according to the stated reward function, from classifiers
that correctly label images with what’s in them, to cars that are starting to
drive on their own. But, as the example of King Midas teaches us, it’s not the
stated reward function that matters: what we really need are algorithms for
generating AI behavior that performs well according to the designer or user’s
intended reward function.
Our recent work on Cooperative
Inverse Reinforcement Learning formalizes and investigates optimal
solutions to this value alignment problem — the joint problem of eliciting
and optimizing a user’s intended objective.
Given an image, humans can easily infer the salient entities in it, and describe the scene effectively, such as, where objects are located (in a forest or in a kitchen?), what attributes an object has (brown or white?), and, importantly, how objects interact with other objects in a scene (running in a field, or being held by a person etc.). The task of visual description aims to develop visual systems that generate contextual descriptions about objects in images. Visual description is challenging because it requires recognizing not only objects (bear), but other visual elements, such as actions (standing) and attributes (brown), and constructing a fluent sentence describing how objects, actions, and attributes are related in an image (such as the brown bear is standing on a rock in the forest).
Current State of Visual Description
LRCN [Donahue et al. ‘15]: A brown bear standing on top of a lush green field.
MS CaptionBot [Tran et al. ‘16]: A large brown bear walking through a forest.
LRCN [Donahue et al. ‘15]: A black bear is standing in the grass.
MS CaptionBot [Tran et al. ‘16]: A bear that is eating some grass.
Descriptions generated by existing captioners on two images. On the left is an image of an object (bear) that is present in training data. On the right is an object (anteater) that the model hasn't seen in training.
Current visual description or image captioning models work quite well, but they can only describe objects seen in existing image captioning training datasets, and they require a large number of training examples to generate good captions. To learn how to describe an object like “jackal” or “anteater” in context, most description models require many examples of jackal or anteater images with corresponding descriptions. However, current visual description datasets, like MSCOCO, do not include descriptions about all objects. In contrast, recent works in object recognition through Convolutional Neural Networks (CNNs) can recognize hundreds of categories of objects. While object recognition models can recognize jackals and anteaters, description models cannot compose sentences to describe these animals correctly in context. In our work, we overcome this problem by building visual description systems which can describe new objects without pairs of images and sentences about these objects.
Over the last few years we have experienced an enormous data deluge, which has
played a key role in the surge of interest in AI. A partial list of some large
- ImageNet, with over 14 million images for classification and object detection.
- Movielens, with 20 million user ratings of movies for collaborative filtering.
- Udacity’s car dataset (at least 223GB) for training self-driving cars.
- Yahoo’s 13.5 TB dataset of user-news interaction for studying human behavior.
Stochastic Gradient Descent (SGD) has been the engine fueling the
development of large-scale models for these datasets. SGD is remarkably
well-suited to large datasets: it estimates the gradient of the loss function on
a full dataset using only a fixed-sized minibatch, and updates a model many
times with each pass over the dataset.
But SGD has limitations. When we construct a model, we use a loss function
$L_\theta(x)$ with dataset $x$ and model parameters $\theta$ and attempt to
minimize the loss by gradient descent on $\theta$. This shortcut approach makes
optimization easy, but is vulnerable to a variety of problems including
over-fitting, excessively sensitive coefficient values, and possibly slow
convergence. A more robust approach is to treat the inference problem for
$\theta$ as a full-blown posterior inference, deriving a joint distribution
$p(x,\theta)$ from the loss function, and computing the posterior $p(\theta|x)$.
This is the Bayesian modeling approach, and specifically the Bayesian Neural
Network approach when applied to deep models. This recent tutorial by Zoubin
Ghahramani discusses some of the advantages of this approach.
The model posterior $p(\theta|x)$ for most problems is intractable (no closed
form). There are two methods in Machine Learning to work around intractable
posteriors: Variational Bayesian methods and Markov Chain Monte Carlo
(MCMC). In variational methods, the posterior is approximated with a simpler
distribution (e.g. a normal distribution) and its distance to the true posterior
is minimized. In MCMC methods, the posterior is approximated as a sequence of
correlated samples (points or particle densities). Variational Bayes methods
have been widely used but often introduce significant error — see this recent
comparison with Gibbs Sampling, also Figure 3 from the Variational
Autoencoder (VAE) paper. Variational methods are also more computationally
expensive than direct parameter SGD (it’s a small constant factor, but a small
constant times 1-10 days can be quite important).
MCMC methods have no such bias. You can think of MCMC particles as rather like
quantum-mechanical particles: you only observe individual instances, but they
follow an arbitrarily-complex joint distribution. By taking multiple samples you
can infer useful statistics, apply regularizing terms, etc. But MCMC methods
have one over-riding problem with respect to large datasets: other than the
important class of conjugate models which admit Gibbs sampling, there has been
no efficient way to do the Metropolis-Hastings tests required by general MCMC
methods on minibatches of data (we will define/review MH tests in a moment). In
response, researchers had to design models to make inference tractable, e.g.
Restricted Boltzmann Machines (RBMs) use a layered, undirected design to
make Gibbs sampling possible. In a recent breakthrough, VAEs use
variational methods to support more general posterior distributions in
probabilistic auto-encoders. But with VAEs, like other variational models, one
has to live with the fact that the model is a best-fit approximation, with
(usually) no quantification of how close the approximation is. Although they
typically offer better accuracy, MCMC methods have been sidelined recently in
auto-encoder applications, lacking an efficient scalable MH test.