T HE I NGREDIENTS OF R EAL -W ORLD R OBOTIC R EINFORCEMENT L EARNING
Anonymous authors
Paper under double-blind review
A BSTRACT
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system.
Subsequently, we investigate a number of challenges of learning without instrumentation – including the lack of episodic resets, state estimation, and hand-engineered rewards – and propose simple, scalable solutions to these challenges.
We demonstrate the efﬁcacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.
I NTRODUCTION
Reinforcement learning (RL) can in principle enable real-world autonomous systems, such as robots, to autonomously acquire a large repertoire of skills.
Perhaps more importantly, reinforcement learning can enable such systems to continuously improve the proﬁciency of their skills from experience.
However, realizing this promise in reality has proven challenging: even with reinforcement learning methods that can acquire complex behaviors from high-dimensional low-level observations, such as images, the typical assumptions of the reinforcement learning problem setting do not ﬁt perfectly into the constraints of the real world.
For this reason, most successful robotic learning experiments have been demonstrated with varying levels of instrumentation, in order to make it practical to deﬁne reward functions (e.g. by using auxiliary sensors (Haarnoja et al., 2018a; Kumar et al., 2016;
Andrychowicz et al., 2018)), and in order to make it practical to reset the environment between trials (e.g. using manually engineered contraptions (Zhu et al., 2019)).
In order to really make it practical for autonomous learning systems to improve continuously through real-world operation, we must lift these constraints and design learning systems whose assumptions match the constraints of the real world, and allow for uninterrupted continuous learning with large amounts of real world experience.
What exactly is holding back our reinforcement learning algorithms from being deployed for learning robotic tasks (for instance manipulation) directly in the real world?
We hypothesize that our current reinforcement learning algorithms make a number of unrealistic assumptions that make real world deployment challenging – access to low-dimensional Markovian state, known reward functions, and availability of episodic resets.
In practice, this means that significant human engineering is required to materialize these assumptions in order to conduct real-world reinforcement learning, which limits the ability of learning-enabled robots to collect large amounts of experience automatically in a variety of naturally occuring environments.
Even if we can engineer a complex solution for instrumentation in one environment, the same may need to be done for every environment being learned in.
When using deep function approximators, actually collecting large amounts of real world experience is typically crucial for effective generalization.
The inability to collect large amounts of real world data autonomously signiﬁcantly limits the ability of these robots to learn robust, generalizable behaviors.
In this work, we propose that overcoming these challenges requires designing robotic systems that possess three fundamental capabilities: (1) they are able to learn from their own raw sensory inputs, (2) they are able to assign rewards to their own behaviors with minimal human intervention, (3) they are able to learn continuously in non-episodic settings without requiring human operators to manually reset the environment.
We believe that a system with these capabilities will bring us signiﬁcantly closer to the goal of continuously improv- ing robotic agents that leverage large amounts of their own real world experience, without requiring signiﬁcant human instrumentation and engineering effort.
Having laid out these requirements, we propose a practical instantiation of such a learning system, which afford the above capabilities.
While prior works have studied each of these issues in isolation, combining solutions to these issues is non-trivial and results in a particularly challenging learning problem.
We provide a detailed empirical analysis of these issues, both in simulation and on a real-world robotic platform, and propose a number of simple but effective solutions that can make it possible to produce a complete robotic learning system that can learn autonomously, handle raw sensory inputs, learn reward functions from easily available supervision, and learn without manually designed reset mechanisms.
We show that this system is well suited for learning dexterous robotic manipulation tasks in the real world, and substantially outperforms ablations and prior work.
While the individual components that we combine to design our robotic learning system are based heavily on prior work, both the combination of these components and their speciﬁc instantiations are novel.
Indeed, we show that without the particular design decisions motivated by our experiments, na¨ıve designs that follow prior work generally fail to satisfy one of the three requirements that we lay out.
T HE S TRUCTURE OF A R EAL -W ORLD RL S YSTEM
Let us start by considering the standard reinforcement learning paradigm, where we operate in a
Markov decision process with state space S , action space A , unknown transition dynamics T , unknown reward function R and an episodic initial state distribution ρ .
In RL, the goal is to learn a policy to maximize the expected sum of rewards via environment interactions
[Figure]
Although this formalism is simple and concise, it does not capture all of the complexities of real-world robotic learning problems.
If a robotic system is to learn continuously and autonomously in the real world, we must ensure that it can learn under all assumptions that are imposed by the real world.
The real world does not have instrumentation available to easily provide low dimensional state estimates, rewards or episodic resets.
To move from the idealized MDP formulation to the real world, we require a system that has the following properties.
Firstly , all of the information necessary for learning must be obtained from the robot’s own sensors.
This includes all information about the state and necessitates that the policy must be learned from high-dimensional and low-level sensory observations, such as camera images.
Secondly , the robot must also obtain the reward signal itself from its own sensor readings.
This is exceptionally difﬁcult for all but the simplest tasks, since reward functions that depend on affecting change in the world to speciﬁc objects require perceiving those objects explicitly.
Thirdly , we must be able to learn in a non-episodic manner, without access to episodic resets.
A setup with explicit resets is increasingly impractical due to the requirement for signiﬁcant human engineering or intervention during learning.
While some of the components discussed above can be tackled in isolation by current algorithms, there are unique challenges inherent to assembling these components into a complete learning system for real world robotics, as well as certain challenges associated with individual components.
In the subsequent discussion, we outline the elements that are required to build a robotic system that can learn in the real world with minimal instrumentation.
These elements present interesting challenges in learning when combined together, which we analyze in Section 3 and address in Section 4.
2. L EARNING FROM R AW S ENSORY I NPUT
To enable learning without complex state estimation systems or instrumenting every environment the robot operates in, we require our robotic systems to be able to learn from their own raw sensory observations.
Typically, these sensory observations are raw camera images from a camera mounted on the robot and proprioceptive sensory inputs such as the joint angles.
These observations do not directly provide the poses of the objects in the scene, which is the typical assumption in simulated robotic environments – any such information must be extracted by the learning system.
While in principle many RL frameworks can support learning from raw sensory inputs (Mnih et al., 2015; Schulman et al., 2015; Lillicrap et al., 2015), it is important to consider the practicalities of this approach.
For instance, we can instantiate vision-based RL with policy gradient algorithms such as TRPO (Schulman et al., 2015) and PPO (Schulman et al., 2017), but these have high sample complexities which make them unsuited for real world robotic learning (Haarnoja et al., 2018a), which is further exacerbated when learning from visual inputs.
In our work, we consider adopting the general framework of off-policy actor-critic reinforcement learning, using a version of the soft actor critic (SAC) algorithm described by (Haarnoja et al., 2018b).
This algorithm effectively uses off-policy data, and has been shown to learn some tasks directly from visual inputs.
However, while
SAC is able to learn directly from raw visual input, most instantiations have required instrumentation for providing rewards and episodic resets.
As we show in Section 3, when these assumptions are lifted it leads to a number of non-trivial learning challenges which require new techniques to address.
2. R EWARD F UNCTIONS WITHOUT R EWARD E NGINEERING
Vision-based RL algorithms, such as SAC, rely on a proper reward function being provided to the system, which is typically hand-deﬁned by a user.
While this can be simple to provide in simulation, it is signiﬁcantly harder to implement in uninstrumented real world environments.
In the real world, the robot must obtain reward signal itself from its own sensor readings, which can be extremely challenging.
A few unappealing options have been suggested to tackle this: engineer complete computer vision systems to detect objects and extract reward signals (Devin et al., 2018;
Nagabandi et al., 2019), engineer reward functions that use various task-speciﬁc heuristics to obtain rewards from pixels (Schenck & Fox, 2017; Kalashnikov et al., 2018), or instrument every environment (Chebotar et al., 2017).
All are highly manual, tedious processes, and a better solution is needed to scale real world robotic learning gracefully.
To devise a system that requires minimal human engineering and supervision for providing rewards, we must use algorithms that are able to assign themselves rewards throughout learning with minimal reward engineering.
One candidate is for a user to specify intended behavior beforehand through example images of desired goals.
The algorithm can then assign itself reward based on a notion of how well it is accomplishing the speciﬁed goals during learning, with no additional human supervision.
This scheme scales well since it requires minimal human engineering, and goal images are easy to provide upfront.
So how exactly might we design such a reward provision system?
To do this, we use a data-driven reward speciﬁcation framework called variational inverse control with events (VICE), introduced by Fu et al.
VICE learns rewards in a task-agnostic way: we provide the algorithm with success examples in the form of images where the task is accomplished, and learn a discriminator that is capable of distinguishing successes from failures.
This discriminator can then be used to provide a learning signal to nudge the reinforcement learning agent towards success.
This algorithm has been previously considered in the context of learning some tasks from raw sensory observations in the real world by (Singh et al., 2019), but we show that it presents unique challenges when used in conjunction with learning without episodic resets.
Details and speciﬁcs of the algorithms being considered are described in Appendix A and also in (Fu et al., 2018; Singh et al., 2019).
2. L EARNING W ITHOUT R ESETS
While the components described in Section 2.1 and 2.2 are essential to building continuously learning RL systems in the real world, they have often been implemented with the assumption of episodic learning.
Indeed, previous applications of Fu et al.
(2018) or Haarnoja et al.
(2018b) were implemented with explicitly designed reset mechanisms or human operators performing resets between trials.
However, natural open-world settings do not provide any such reset mechanism, and in order to enable scalable and autonomous real-world learning, we need systems that do not require an episodic formulation of the learning problem.
In principle, algorithms such as SAC do not actually require episodic learning; however, in practice, most instantiations of these algorithms have used explicit resets, even in simulation, and removing resets has resulted in failure to solve challenging tasks.
While RL algorithms can handle nonepisodic settings without any modiﬁcations in principle, they struggle when applied in practice to challenging problems.
In our experiments in Section 3, we see that simply applying actor-critic methods to the reset free setting doesn’t learn intended behaviors and requires novel insights when combined with visual observations and classiﬁer based rewards.
These three components – vision-based RL with actor-critic algorithms, vision-based goal classiﬁer for rewards, and reset-free learning – are the fundamental pieces that we need to build a real world robotic learning system.
However, when we actually combine the individual components in
Sections 3 and 6, we ﬁnd that learning effective policies is quite challenging.
We provide insight into these challenges in Section 3 and, based on these insights, we propose a number of simple but important changes in Section 4 that enable R3L to learn effectively and autonomously in the real world without human intervention.
C HALLENGES OF R EAL W ORLD RL
The system design outlined in Section 2, in principle, gives us a complete system to perform real world reinforcement learning without instrumentation.
However, when ported to robotic problems in the real world, we ﬁnd this basic design to be largely ineffective.
To illustrate this, we present results for a simulated robotic manipulation task that requires repositioning a free-ﬂoating objects with a three-ﬁngered robotic hand, shown in Fig 3.
We use this task for our investigative analysis, and show that the same insights extend to several other tasks (including real world tasks) in Section 6.
The goal in this task is to reposition the object to a target pose from any initial pose in the arena.
When the system is instantiated with vision-based SAC, rewards from goal images using
VICE and run without episodic resets, we see that the algorithm fails to make progress (Fig 4).
Although it might appear that this setup ﬁts within the assumptions of all of the components that are used, the complete system is ineffective.
Which particular components of this problem make it so difﬁcult?
To investigate this issue, we set up experiments that combine the three main ingredients: varying observation type (visual vs. low-dimensional state), reward structure (VICE vs. hand-deﬁned rewards that utilize ground-truth object state) and the ability to reset (episodic resets vs. reset-free, non-episodic learning).
We start by considering the training time reward under each combination of factors as shown in Fig 4.
This reveals several trends: ﬁrst, the results in Fig 4 show that learning with resets gets
[Figure]
EvaluationTraining good training time reward with both vision and state while reset-free only obtains good training time reward with low-dimensional state; second, we ﬁnd that the policy is able to pass the threshold for training time rewards in a non-episodic setting when operating from low-dimensional state but not when using image observations.
This suggests that combining the reset-free learning problem with visual observations makes it signiﬁcantly more challenging than with low-dimensional state.
However, the table in Fig 4 paints an incomplete picture.
These numbers are related to the performance of the policies at training time, not how effective the learned policies are when being evaluated.
When we consider the test-time performance (Fig 5) of the learned policies under reset free conditions, we obtain a different set of conclusions.
While learning from low-dimensional state in the reset free setting is able to achieve decent training time reward, the test-time performance of the corresponding learned policies is very poor.
This can likely be attributed to the fact that when the agent spends all its time stuck at the goal, it sees very little diversity of data in other parts of the state space, which signiﬁcantly affects the efﬁcacy of the actual policies being learned.
This makes it very challenging to learn policies with completely reset-free schemes, which has prompted prior work to consider schemes such as learning reset-controllers (Eysenbach et al., 2018).
As we discuss in the following section and in our experiments, these schemes are often insufﬁcient for learning effective policies in real world without any resets.
These insights prompt us to propose simple solutions for instrumentation free reinforcement learning in Section 4.
R3L: R EAL - WORLD R OBOTIC R EINFORCEMENT L EARNING
To address the challenges identiﬁed in Section 3, we present two improvements to the basic system outlined above, which we found to be essential for uninstrumented real-world training: randomized perturbation controllers and unsupervised representation learning.
Incorporating these components into the system in Section 2 results in a method that can learn in uninstrumented environments, as we will show in Section 6.
4. R ANDOM P ERTURBATION C ONTROLLER
From our observations in Fig 4, we can see that it is not effective to simply perform reset-free RL using a standard actor critic algorithm.
Even the policies trained with full state information do not actually learn how to perform the desired repositioning task, as shown in Fig 5.
This is because, once the policy has performed the task once, it does not need to perform it again, and therefore not only fails how to learn to perform the task reliably, but in fact tends to forget how to perform it at all.
Prior work has considered addressing this problem by converting the reset-free learning problem into a more standard episodic problem, by learning a “reset controller,” which is trained to reset the system to a particular initial state (Eysenbach et al., 2018; Han et al., 2015).
However, as we will show in our experiments in Section 6, this still results in policies that only succeed from a very narrow range of initial states.
Indeed, prior reset controller methods all reset to a single initial state (Eysenbach et al., 2018; Han et al., 2015).
We take a different approach to learning in a reset-free setting.
Rather than attributing the problem to the variance of the initial state distribution, we hypothesize that a major problem with reset-free learning is that the support of the distribution of states visited by the policy is extremely narrow, which makes the learning problem challenging and doesn’t allow the agent to learn how to perform the desired task from any state it might ﬁnd itself in.
In this view, the goal should not be to reduce the variance of the initial state distribution, but instead to increase it.
To this end, we utilize what we call random perturbation controllers: controllers that introduce perturbations intermittently into the system through a policy that is trained to explore the environment.
The standard actor π ( a | s ) is executed for H time-steps, following which we executed the perturbation controller π p ( a | s ) for H steps, and repeat.
The policy π is trained with the VICE-based rewards for reaching the desired goals, while the perturbation controller π p is trained only with an intrinsic motivation objective that encourages visiting under-explored states.
In our implementation, we use the random network distillation (RND) objective for training the perturbation controller (Burda et al., 2018).
This procedure is described in detail in Appendix A, and is evaluated on the tasks we consider in Fig 6.
The perturbation controller ensures that the support of the training distribution grows, and as a result the policies can learn the desired behavior much more effectively, as shown in Fig 7.
4. U NSUPERVISED R EPRESENTATION L EARNING
The perturbation controller discussed above allows us to learn policies that can succeed at the task from a variety of starting states.
However, learning from visual observations still present a challenge.
Our experiments in Fig 4 show that learning without resets from low-dimensional state is comparatively easier.
We therefore aim to convert the vision-based learning problem into one that more closely resembles state-based learning, by training a variational autoencoder (VAE) and sharing the latent-variable representation across the actor and critic networks.
Refer to Appendix B for more details.
Note that we use a VAE as an instantiation of representation learning techniques that works well in the domains considered; approaches proposed by Lee et al.
, Hjelm et al.
and Anand et al.
(2019) are also viable substitutes in this regard.
While several prior works have also sought to incorporate unsupervised learning into reinforcement learning to make learning from images easier (Nair et al., 2018; Lee et al., 2019), we note that this becomes especially critical in the vision-based, reset-free setting, as motivated by the experiments in Section 3, which indicate that it is precisely this combination of factors – vision and no resets – that presents the most difﬁcult learning problem.
Therefore, although the particular solution we use in our system has been studied in prior work, it is brought to bare to address a challenge that arises in real-world learning that we believe has not been explored in prior studies.
These two improvements – the perturbation controller and joint training with an unsupervised learning loss, – combined with the general system described above, give us a complete practical system for real world reinforcement learning, which we term R3L .
The overall method uses soft-actor critic for learning with visual observations and classiﬁer based rewards with VICE, introduces auxiliary reconstruction objectives for unsupervised representation learning, and uses a perturbation controller during training to ensure that the learned policy can accomplish the task from a wide variety of states.
Further details on the full system can be found in Appendix A.
R ELATED W ORK
The primary contribution of this work is to propose a paradigm for continual instrumentation-free real world robotic learning, and a practical instantiations of such a system.
Several prior works have applied policies learned with RL to particular tasks in the real world (Kalashnikov et al., 2018;
Gu et al., 2017; Zhu et al., 2019; Nagabandi et al., 2019; Haarnoja et al., 2018b).
While many of these algorithms simply train in simulation and transfer resulting policies to the real world, this paradigm is prone to domain shift and extensive simulation efforts (Sadeghi & Levine; Tobin et al., 2017; Andrychowicz et al., 2018).
We instead focus on the paradigm of reinforcement learning purely in the real world.
While algorithms have shown that we can indeed perform RL in the real world (Kalashnikov et al., 2018;
Gu et al., 2017; Zhu et al., 2019; Nagabandi et al., 2019;
Haarnoja et al., 2018b; Kumar et al., 2016), these have been limited to highly instrumented laboratory settings.
They have carefully hand-designed reward assignment schemes (Levine & Koltun, 2013), reset mechanisms (Gu et al., 2017; Chebotar et al., 2017; Zhu et al., 2019).
In contrast, we are proposing to learn in environments with signiﬁcantly less human instrumentation.
This introduces a unique set of challenges not considered carefully in prior works.
A key component of our system is learning from raw visual inputs.
This has proven to be extremely difﬁcult for policy gradient style algorithms (Pinto et al., 2017a)
due to challenging representation learning problems.
This has been made easier in simulated domains by using modiﬁed objectives such as auxiliary losses (Jaderberg et al., 2016) or by using more efﬁcient algorithms (Haarnoja et al., 2018a).
We show that reinforcement learning on raw visual input, while possible in standard
RL settings, becomes signiﬁcantly more challenging when considered in conjunction with nonepisodic, reset-free scenarios.
Reward function design is crucial for any RL system, and is non-trivial to provide in the real world.
Prior works have considered instrumenting the environment with additional sensors to evaluate rewards (Gu et al., 2017; Chebotar et al., 2017; Zhu et al., 2019), which is a highly manual process, using demonstrations, which require manual effort to collect (Vecerik et al., 2017; Ng & Russell, 2000;
Liu et al., 2018), or using interactive supervision from a user (Christiano et al., 2017).
In this work, we leverage the algorithm introduced by Fu et al.
(2018) to assign rewards based on the likelihood of a goal classiﬁer.
While prior work also applied this method to robotic tasks (Singh et al., 2019), this was done in a setting where manual resets were provided, while we demonstrate that we can use learned rewards in a fully uninstrumented, reset-free setup.
Learning without resets has been considered in prior works (Eysenbach et al., 2018; Han et al., 2015), although in different contexts – safe learning and learning compound controllers respectively.
Eysenbach et al.
(2018) provide an algorithm to learn a reset controller with the goal of ensuring safe operation, but makes several assumptions that make it difﬁcult to use in the real world: it assumes access to a ground truth reward function, it assumes access to an oracle function that can detect if an attempted reset by the reset policy was successful or not, and it assumes the ability to perform manual resets if the reset policy fails a certain number of times.
In contrast, we propose an algorithm that allows for fully automated reinforcement learning in the real world.
We compare to an ablation of our method that uses a reset controller similar to Eysenbach et al.
(2018), and show that our method performs substantially better.
Our perturbation controller also resembles the adversarial RL setup Pinto et al.
(2017b);
Sukhbaatar et al.
However, while these prior methods explicitly aim to train policies that are robust to perturbations Pinto et al.
(2017b)
or explore effectively Sukhbaatar et al.
(2018), we are concerned with learning without access to resets.
While this line of work has connections to developmental robotics (Lungarella et al., 2003; Asada et al., 2009) and it’s subﬁelds such as continual (Lesort et al., 2019) and lifelong (Thrun & Mitchell, 1995) learning, the goal of our work is to handle the practicalities of enabling reinforcement learning systems to learn in the real world without instrumentation or interruption, even for a single task setting, without multi-task considerations.
The insights we make should also be applicable to improve developmental robotics algorithms.
Though these settings don’t touch on all aspects of developmental robotics, our work does relate with the subﬁelds of continual learning (Lesort et al., 2019), intrinsic motivation (Schmidhuber, 2006) and sensory-motor development involving proprioceptive manipulation (Stoica, 2001).
E XPERIMENTS
In our experimental evaluation, we study how well the R3L system, described in Sections 2 and 4, can learn under realistic settings – visual observations, no hand-speciﬁed rewards, and no resets.
We consider the following hypotheses:
Can we use R3L to learn complex robotic manipulation tasks without instrumentation?
Does this system learn skills in both simulation and the real world?
Do the solutions proposed in Section 4 actually enable R3L to perform tasks without instrumentation that would not have been otherwise possible?
6. E XPERIMENTAL S ETUP
We consider the task of dexterous manipulation with a three ﬁngered robotic hand, called the
D’Claw (Zhu et al., 2019; Ahn et al., 2019), on a number of simulated and real world environments.
These tasks involve complex coordination of three ﬁngers with 3 DoF each in order to manipulate objects.
Prior works that used this robot utilized explicit resets and low-dimensional true state observations, while we consider settings with visual observations, no hand-speciﬁed rewards, and no resets.
The tasks in our experiments are shown in Fig 6: manipulating beads on an abacus row, valve rotation, and free object repositioning.
These tasks represent a wide class of problems that robots might encounter in the real world.
For each task, we consider the problem of reaching the depicted goal conﬁguration: moving the abacus beads to a particular position, rotating the valve to a particular angle, and repositioning the free object to a particular goal position.
For each task, policies are evaluated from a wide selection of initial conﬁgurations.
Additional details about the tasks, and the evaluation procedures, including initial conﬁgurations and success metrics are provided in
Appendix C. Videos and additional details can be found at https://sites.google.com/
view/realworld-rl/ . 6. L EARNING IN S IMULATION WITHOUT I NSTRUMENTATION
We compare our entire proposed system implementation (Section 4) with a number of baselines and ablations.
Importantly, all methods must operate under the same assumptions: no system instrumentation for state estimation, reward speciﬁcation, or episodic resets.
Firstly, we compare the performance of R3L to a system which uses SAC for vision-based RL from raw pixels, VICE for providing rewards and running reset-free (denoted as “VICE”).
This corresponds to the vanilla version of R3L (Section 2), with none of the proposed insights and changes.
We then compare with prior reset-free RL algorithms (Eysenbach et al., 2018) that explicitly learn a reset controller to alternate goals in the state space (“Reset Controller + VAE”).
Lastly, we compare algorithm performance with two clear ablations - running R3L without the perturbation controller (“VICE + VAE”) and without the unsupervised learning (“R3L w/o VAE”).
This highlights the signiﬁcance of all the components of R3L .
From the experimental results in Fig 7, it is clear that R3L is able to reach the best performance across tasks, while none of the other methods are able to solve all of the tasks.
Different prior methods and ablations fail for different reasons: (1) methods without the reconstruction objective struggle at parsing the high-dimensional input and are unable to solve the harder task; (2) methods without the perturbation controller are ineffective at learning how to reach the goal from novel initialization positions for the more challenging object repositioning tasks, as discussed in Section 4.
We note that an explicit reset controller, which can be viewed as a softer version of our perturbation controller with goal-directedness, learns to solve the easier tasks due to the reset controller encouraging exploration of the state space.
In our experiments for free object repositioning, performance was reported across 3 choices of reset states.
The high variance in evaluation performance indicates that the performance of such a controller (or a goal conditioned variant of it) is highly sensitive to the choice of reset states.
A poor choice of reset states, such as two that are very close together, may yield poor exploration leading to performance similar to the single goal VICE baseline.
Furthermore, the choice of reset states is highly task dependent and it is often not clear what choice of goals will yield the best performance.
On the contrary, our method does not require such taskspeciﬁc knowledge and uses random perturbations to reset while training: this allows for a robust, instrumentation-free controller while also ensuring fast convergence.
6. L EARNING IN THE R EAL W ORLD WITHOUT I NSTRUMENTATION
Since the aim of R3L is to enable uninstrumented training in the real world, we next evaluate our method on a real-world robotic system, providing evidence that our insights generalize to the real world without any instrumentation .
In the instrumentation-free setting, we leave the robot unattended with a set of goal images provided and the algorithm learns the desired behavior through interaction.
The experiments are performed on the DClaw robotic hand with an RGB camera as the only sensory input.
Intermediate policies are saved at regular intervals, and evaluations of all policies are performed after training has completed.
For valve rotation, we declare an evaluation rollout a success if the ﬁnal orientation is within within 15 ◦ of the goal.
For bead manipulation, we declare success if all the beads are within 2 cm of the goal state.
Fig 8 compares the performance of our method without supervised learning (“R3L w/o VAE”) in the real-world against a baseline that uses SAC for vision-based RL from raw pixels, VICE for providing rewards and running reset-free (denoted as “VICE”).
We see that our method learns policies that evaluate from nearly all the initial conﬁgurations, whereas VICE fails to evaluate from most initial conﬁgurations.
Fig 9 depicts sample evaluation rollouts of the policies learned using our method.
For further details about real world experiments see Appendix C.2.
C ONCLUSION
We presented the design and instantiation of R3L , a system for real world reinforcement learning.
We identify and investigate the various ingredients required for such a system to scale gracefully with minimal human engineering and supervision.
We show that this system must be able to learn from raw sensory observations, learn from very easily speciﬁed reward functions without reward engineering, and learn without any episodic resets.
We describe the basic elements that are required to construct such a system, and identify unexpected learning challenges that arise from interplay of these elements.
We propose simple and scalable ﬁxes to these challenges through introducing unsupervised representation learning and a randomized perturbation controller.
We show the effectiveness on such a system at learning without instrumentation in several simulated and real world environments.
The ability to train robots directly in the real world with minimal instrumentation opens a number of exciting avenues for future research.
Robots that can learn unattended, without resets or handdesigned reward functions, can in principle collect very large amounts of experience autonomously, which may enable very broad generalization in the future.
Furthermore, fully autonomous learning should make it possible for robots to acquire large behavioral repertoires, since each additional task requires only the initial examples needed to learn the reward.
However, there are also a number of additional challenges, including sample complexity, optimization and exploration difﬁculties on more complex tasks, safe operation, communication latency, sensing and actuation noise, and so forth, all of which would need to be addressed in future work in order to enable truly scalable realworld robotic learning.
A A LGORITHM DETAILS
1: procedure R3L 2:
N ← number of training epochs 3:
H ← trajectory length (horizon) 4: n VICE ← number of VICE classiﬁer training iterations per epoch 5:
Initialize forward and perturbing policies π 0 , π6:
Obtain goal states s Ei and initialize as a goal pool G 7:
Initialize RND target and predictor networks f ( s ) , ˆ f ( s ) 8:
Initialize VICE reward classiﬁer r VICE ( s ) 9:
Initialize replay buffer D 10:
Collect initial exploration data and add to D 11: for i = 1 to N do 12: k ← i %13: for t = 1 to H do 14:
Sample a t ∼ π k ( s t ) 15:
Sample s t +1 ∼ p ( s t +1 | s t , a t ) 16: if k ==
then 17: r t ( s t ) =
c VICE ∗ r VICE ( s t ) + c RND ∗ r RND ( s t ) 18: else if k == 1 then 19: r t ( s t ) =
r RND ( s t ) 20: end if 21:
Sample batch from D 22:
Update π k with batch according to SAC 23:
Update RND predictor network with batch 24:
Update running estimate of classiﬁer and RND reward standard deviations 25: end for 26:
Add experience to the replay buffer with D ← D ∪ τ i 27:
Sample an equal number of goal examples from G and negative examples from D 28: for t = 1 to n VICE do 29:
Train the VICE classiﬁer on this batch with binary labels 30: end for 31: end for 32: end procedure
B T RAINING DETAILS B.0. H YPERPARAMETERS
General
Standard deviation up- date coefﬁcient 0.
Image Sizes [(16, 16, 3), (32, 32, 3) , (64, 64, 3)]
SAC
Learning Rate 3eγ 0.
Batch Size
Convnet Filters [ (64, 64, 64) , (16, 32, 64)]
Stride (2, 2)
Kernel Sizes (3, 3)
Pooling [MaxPool2D, None ]
Actor/Critic FC Layers [ (512, 512) , (256, 256, 256)]
VICE n VICE [1, 5 , 10]
Batch Size
Learning Rate 1eMixup α
Uniform(0, 1)
Convnet Filters [ (64, 64, 64) , (16, 32, 64)]
Stride (2, 2)
Kernel Sizes (3, 3)
Pooling [MaxPool2D, None ]
FC Layers [ (512, 512) , (256, 256, 256)]
RND
Learning Rate 3eBatch Size
Convnet Filters (16, 32, 64)
Stride (2, 2)
Kernel Sizes (3, 3)
Pooling [MaxPool2D, None ]
FC Layers [ (512, 512) , (256, 256, 256)]
VAE
Learning Rate 1eBatch Size
Encoder (Convnet) (64, 64, 32)
Latent Dimension [8, 16, 32 , 64] β
[1e-3, 0.1, 0.5 , 1, 10]
Stride (2, 2)
Kernel Sizes (3, 3)
Pooling [MaxPool2D, None ]
The ranges of values listed above represent the hyperparameters we searched over, and the bolded values are what we use in the Section 6 experiments.
B.0. VICE
We use a variant of VICE which deﬁnes the reward as the logits of the classiﬁer, notably omitting the − log ( π ( a | s )) term.
We also regularize our classiﬁer with mixup (Zhang et al., 2018).
We train all of our experiments using 200 goal images, which takes under an hour to collect in the real world for each task.
B.0. R ANDOM N ETWORK D ISTILLATION (RND)
We found it important to normalize the predictor errors, just as (Burda et al., 2018) did.
B.0. VAE
We train a standard beta-VAE to maximize the evidence lower bound, given by:
[Equation]
To collect training data, we sampled random states in the observation space.
In the real world, this sampling can be replaced with training an exploratory policy (i.e. using the RND reward as the policy’s only objective).
The learned weights of the encoder of the VAE are frozen, and the latent input is used to train the policy for reset-free RL.
C T ASK DETAILS C. S IMULATED T ASKS
We evaluated our system across three tasks in simulation: bead manipulation, valve rotation, free object repositioning
. C.1. B EAD M ANIPULATION
The bead manipulation task involves an abacus rod with four beads that can slide freely.
The goal is to position two beads on each end from any initial conﬁguration of beads.
This can take the form of sliding one bead over (if three beads start on one side), two beads over (if all four beads start on one side), splitting beads apart (all four beads start in the middle), or some intermediate combination of those.
The true reward is deﬁned as the mean goal distance of all four beads.
Policies are evaluated starting from the 8 initial conﬁgurations depicted in Fig 10.
Evaluation performance reported in
Section 6 for this task is deﬁned as the ﬁnal reward averaged across the 8 evaluation rollouts.
C.1. V ALVE R OTATION
The claw is positioned above a three pronged valve (15 cm in diameter).
The objective is to turn the valve to a given orientation from any initial orientation.
The ”true reward” is r = − log( | θ state − θ goal | ) .
Policies are evaluated starting from the 8 initial conﬁgurations depicted in Fig 11.
Evaluation performance reported in Section 6 for this task is deﬁned as the ﬁnal orientation distance averaged across the 8 evaluation rollouts.
C.1. F REE O BJECT R EPOSITIONING
The claw is positioned atop a free (6 DoF) three pronged object (15cm in diameter), which can translate and rotate within a 30 cm x 30 cm box.
The goal is speciﬁed by a xy-position as well as a z-angle, where the xy-plane is the plane of the arena.
The true reward is deﬁned as the weighted sum of the angular and translational distances, r = − 2 log
( || [ x state , y state ] − [ x goal , y goal ] || 2 ) − log( | θ state − θ goal | ) .
In our experiments, ( x, y, θ ) goal = (0 , 0 , − π 2 ) , where the origin is centered in the arena.
Policies are evaluated starting from the 15 initial conﬁgurations depicted in Figure 12.
Evaluation performance reported in Section 6 for this task is deﬁned as the ﬁnal pose distance (
[Figure]
) averaged across the 15 evaluation rollouts.
In our reset controller experiments, we averaged evaluation performance over three different choices of reset states, where the ﬁrst reset state is always the goal:
( x, y, θ ) reset, 1 = ( x, y, θ ) goal , ( x, y, θ ) reset, 2 = (0 . 05 , 0 . 05 ,
[Figure]
( x, y, θ ) reset, 1 = ( x, y, θ ) goal , ( x, y, θ ) reset
[Figure]
2 = (
[Figure]
[Figure]
, −
[Figure]
( x, y, [Figure]
[Figure]
1 =
[Figure]
x, y
[Figure]
goal ,
[Figure]
x,
[Figure]
[Figure]
C. R EAL W ORLD T ASKS
For each setup we use an RGB camera to get images.
We execute actions on the DClaw at 10Hz.
In order to operate at such a high frequency while also training from images we sample and train asynchronously, but limit training to not exceed two gradient steps per transition sampled in the real world.
Since direct performance metrics cannot be measured during training due to the lack of object instrumentation, evaluations of performance are done post-training.
C.2. V ALVE R OTATION
The task is identical to the one in simulation.
Evaluations were done post-training.
An evaluation trajectory was deﬁned as a success if at the last step, the valve was within 15 degrees of the goal.
Each policy was evaluated over 8 rollouts, with initial conﬁgurations evenly spaced out between at increments of 45 degrees.
Results are reported in Figures 13, 14.
C.2. B EAD M ANIPULATION
The rod is 22cm in length, and each bead measures 3.5cm in diameter.
Evaluations were done post-training, using the following procedure: the environment was manually reset to each of thespeciﬁed conﬁgurations shown in Figures 15 and 16 (which cover a full range of the state space) at the start of each evaluation rollout.
An evaluation trajectory was deﬁned as a success if at the last time step, all beads were within 2cm of their goal positions.
Performance was evaluated at around 20 hours, at which point the policy achieved greater than 80% success on the 10 evaluation rollouts (a random policy achieved a success rate of 10%).
Results are reported in Figs 15, 16.