Domain Randomization and Generative Models for Robotic Grasping

Domain Randomization and Generative Models for Robotic Grasping

Josh Tobin, Wojciech Zaremba, Pieter Abbeel OpenAI and UC Berkeley EECS, OpenAI, UC Berkeley EECS & ICSI,

Deep learning-based robotic grasping has made significant progress the past several years 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 , where corresponds to the model’s estimate of the normalized probability of success of the grasp conditioned on the observations . Our 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 find we can achieve 90% success rate on previously unseen realistic objects at test time despite having only been trained on random objects.

I Introduction

Robotic grasping remains one of the core unsolved problems in manipulation. The earliest robotic grasping methods used analytical knowledge of a scene to compute an optimal grasp for an object [30, 1, 31, 37, 38, 44]. Assuming a simplified contact model and a heuristic for the likelihood of success of a grasp, analytical methods can provide guarantees about grasp quality relative to the chosen heuristic, but they often fail in the real world due to inconsistencies in the simplified object and contact models, the need for accurate 3D models of the objects in question, and sensor inaccuracies [2].

As a result, significant research attention has been given to data-driven grasp synthesis methods [2, 26, 34, 8, 28, 27, 42]. These algorithms avoid some of the challenges of analytic methods by sampling potential grasps and ranking them according to a learned function that maps sensor inputs to an estimate of a chosen heuristic. Data-driven grasp synthesis algorithms can be characterized by (a) how they sample grasps, (b) which heuristic they use to rank grasps, and (c) how they estimate the value of the chosen heuristic from camera images and other sensor data.

Recently, several works have explored using deep neural networks to approximate the grasp heuristic function [22, 35, 23, 14]. The promise of deep neural networks for learning grasp heuristics is that with diverse training data, deep models can learn features that deal with the edge cases that make real-world grasping challenging.

A core challenge for deep learning grasp quality heuristics is data availability. Due to the difficulty and expense of collecting real-world data and due to the limited availability of high-quality 3D object meshes, current approaches use as few as hundreds or thousands of unique object instances, which may limit generalization. In contrast, ImageNet [17], the standard benchmark for image classification, has about 15 million unique images from 22 thousand categories.

In order to increase the availability of training data in simulation, we explore applying the idea of domain randomization [40, 45] to the creation of 3D object meshes. Domain randomization is a technique for learning models that work in a test domain after only training on low-fidelity simulated data by randomizing all non-essential aspects of the simulator. One of the core hypotheses of this work is that by training on a wide enough variety of unrealistic procedurally generated object meshes, our learned models will generalize to realistic objects.

Previous work in deep learning for grasping has focused on learning a function that estimates the quality of a given grasp given observations of the scene. Choosing grasps on which to perform this estimate has received comparatively little attention in the deep learning and robotics community, where it is typically done using random sampling or by solving a small optimization problem online. The second goal of this paper is to propose a deep learning-based method for choosing grasps to evaluate. Our hypothesis is that a learned model for grasp sampling will be more likely to find high-quality grasps for challenging objects and will do so more efficiently.

We use an autoregressive model architecture [19, 33, 32] that maps sensor inputs to a probability distribution over grasps that corresponds to the model’s weighted estimate of the likelihood of success of each grasp. After training, highest probability grasp according to the distribution succeeds on 89% of test objects and the 20 highest probability grasps contain a successful grasp for 96% of test objects. In order to determine which grasp to execute on the robot, we collect a second observation in the form of an image from the robot’s hand camera and train a second model to choose the most promising grasp among those sampled from the autoregressive model, which results in a success rate of 92%.

The contributions of this paper can be summarized as follows:

  • We explore the effect of training a model for grasping using unrealistic procedurally generated objects and show that such a model can achieve similar success to one trained on a realistic object distribution. (Another paper [3] developed concurrently to this one explored a similar idea and reached similar conclusions.)

  • We propose a novel generative model architecture and training methodology for learning the normalized probability of a grasp success conditioned on observation(s) of an object and choosing a high quality grasp at test time.

  • We evaluate our object generation, training, and sampling algorithms in simulated scenes and find that we can achieve 84% success rate on random objects and 92% success rate on previously unseen real-world objects despite training only on non-realistic randomly generated objects. With only a single sample from the autoregressive model, we can reach 76% for random objects and 89% for realistic objects.

Ii Related Work

Ii-a Domain Randomization

Domain randomization involves randomizing non-essential aspects of the training distribution in order to better generalize to a difficult-to-model test distribution. This idea has been employed in robotics since at least 1997, when Jakobi proposed the “Radical Envelope of Noise Hypothesis”, the idea that evolved controllers can be made more robust by completely randomizing all aspects of the simulator that do not have a basis in reality and slightly randomizing all aspects of the simulator that do have a basis in reality [13]. Recently domain randomization has shown promise in transferring deep neural networks for robotics tasks from simulation to the real world by randomizing physics [29] and appearance properties [40, 45, 52].

In another work developed concurrently with this one [3], the authors reach a similar conclusion about the utility of procedurally generated objects for the purpose of robotic grasping. In contrast to this work, theirs focuses on how to combine simulated data with real grasping data to achieve successful transfer to real world grasping, but does not focus on achieving a high overall success rate. Our paper instead focuses on how to achieve the best possible generalization to novel objects, but limits its focus to simulation.

Ii-B Autoregressive models

This paper uses an autoregressive architecture to model a distribution over grasps conditioned on observations of an object. Autoregressive models leverage the observation that an -dimensional probability distribution can be factored as for any choice of ordering of -dimensional variables . The task of modeling the distribution then consists of modeling each [19]. In contrast to Generative Adversarial Networks [9], another popular form for a deep generative model, autoregressive models can directly compute the likelihood of samples, which is advantageous for tasks like grasping in which finding the highest likelihood samples is important. Autoregressive models have been used for density estimation and generative modeling in image domains [19, 10, 7] and have been shown to perform favorably on challenging image datasets like ImageNet [33, 46]. Autoregressive models have also been successfully applied to other forms of data including in topic modeling [18] and audio generation [32].

Ii-C Robotic grasping

Grasp planning methods fall into one of two categories: analytical methods and empirical methods [41].

Analytical methods use a contact model and knowledge of an object’s 3D shape to find grasps that maximize a chosen metric like the ability of the grasp to resist external wrenches [37] or constrain the object’s motion [38]. Some methods attempt to make these estimates more robust to gripper and object pose uncertainty and sensor error by instead maximizing the expected value of a metric under uncertainty [15, 48].

Most approaches use simplified Coulomb friction and rigid body modeling for computational tractability [30, 37], but some have explored more realistic object and friction models [1, 36, 39]. Typically, grasps for an object are selected based on sensor data by registering images to a known database of 3D models using a traditional computer vision pipeline [6, 12, 49] or a deep neural network [11, 51].

Empirical methods instead attempt to maximize the value of a quality metric through sampling [41]. Many approaches use simulation to evaluate classical grasp quality metrics [26, 34, 8, 25]. Others use human labeling or self-supervised learning to measure success [28, 27, 42].

Most techniques estimate the value of the quality metric in real-world trials using traditional computer vision and learning techniques [43, 20, 42, 34]. More recently, deep learning has been employed to learn a mapping directly from sensor observations to grasp quality or motor torques.

Ii-D Deep learning for robotic grasping

Work in deep learning for grasping can be categorized by how training data is collected and how the model transforms noisy observations into grasp candidates.

Some approaches use hand-annotated real-world grasping trials to provide training labels [21]. However, hand-labeling is challenging to scale to large datasets. To alleviate this problem, some work explores automated large-scale data collection [22, 35]. Others have explored replacing real data with synthetic depth data at training time [23, 24, 47, 14], or combining synthetic RGB images with real images [3]. In many cases, simulated data appears to be effective in replacing or supplementing real-world data in robotic grasping. Unlike our approach, previous work using synthetic data uses small datasets of up to a few thousand of realistic object meshes.

One commonly used method for sampling grasps is to learn a visuomotor control policy for the robot that allows it to iteratively refine its grasp target as it takes steps in the environment. Levine and co-authors learn a prediction network that takes an observation and motor command and outputs a predicted probability of a successful grasp if is executed [22]. The cross-entropy method is used to greedily select the that maximizes . Viereck and co-authors instead learn a function that maps the current observation and an action to an estimate of the distance to the nearest successful grasp after performing [47]. Directions are sampled and a constant step size is taken in the direction with the minimum value for . In contrast to visuomotor control strategies, planning approaches like ours avoid the potential local optima of greedy execution.

Another strategy to choose a grasp using a deep learning is to sample grasps and score them using a deep neural network of the form , where are the observation(s) of the scene, is a selected grasp, and is the score for the selected grasp [23, 24, 14, 50]. These techniques differ in terms of how they efficiently sample grasps to evaluate at test time. Most commonly these techniques directly optimize using the cross-entropy method [23, 24, 50]. In contrast to these approaches, our approach jointly learns a grasp scoring function and a sampling distribution, allowing for efficient sampling and avoiding potential exploitation by the optimization procedure of under- or over-fit regions of the grasp score function.

Other approaches take a multi-step approach, starting with a coarse representation of the possible grasps for an object and then exhaustively searching using a learned heuristic [21] or modeling the score function jointly for all possible coarse grasps [14]. Once a coarse grasp is sampled, it is then fine-tuned using a separate network [21] or interpolation [14]. By using an autoregressive model architecture, we are able to directly learn a high- dimensional ( or -dimensional) multimodal probability distribution.

Iii Method

Our goal is to learn a mapping that takes one or more observations of a scene and outputs a grasp to attempt in the scene. The remainder of the section describes the data generation pipeline, model architecture, and training procedure used in our method.

Iii-a Data collection

Fig. 1: Examples of randomly generated objects used in our experiments. Left: procedurally generated random objects. Middle: objects from the ShapeNet object dataset. Right: objects from the YCB object dataset.

We will first describe the process of generating training objects, and then the process of sampling grasps for those objects.

Iii-A1 Object generation

One of our core hypotheses for this project is that training on a diverse array of procedurally generated objects can produce comparable performance to training on realistic object meshes. Our procedurally generated objects were formed as follows:

  1. Sample a random number

  2. Sample primitive meshes from our object primitive dataset

  3. Randomly scale each primitive so that all dimensions to between 1 and 15cm

  4. Place the meshes sequentially so that each mesh intersects with at least one of the preceding meshes

  5. Rescale the final object to approximate the size distribution observed in our real object dataset

To build a diverse object primitive dataset, we took the more than 40,000 object meshes found in the ShapeNet object dataset [5] and decomposed them into more than 400,000 convex parts using V-HACD111 Each primitive is one convex part.

We compared this object generation procedure against a baseline of training using rescaled ShapeNet objects.

Iii-A2 Grasp sampling and evaluation

We sample grasps uniformly at random from a discretized - or -dimensional grasp space (-dimensional when attention is restricted to upright grasps) corresponding to the coordinates of the center of the gripper and an orientation of the gripper about that point. We discretize each dimension into buckets. The buckets are the relative location of the grasp point within the bounding box of the object – e.g., an -value of corresponds to a grasp at the far left side of the object’s bounding box and a coordinate of corresponds to a grasp at the far right.

Grasps that penetrate the object or for which the gripper would not contact the object when closed can be instantly rejected. The remainder are evaluated in a physics simulator.

For each grasp attempt, we also collect a depth image from the robot’s hand camera during the approach to be used to train the grasp evaluation function.

Iii-B Model architecture

Fig. 2: An overview of sampling from our model architecture. Solid lines represent neural networks, and dotted lines represent sampling operations. The model takes as input one or more observations of the target object in the form of depth images. The images are passed to an image representation module , which maps the images to an embedding . The embedding is the input for the autoregressive module , which outputs a distribution over possible grasps for the object. We sample high-likelihood grasps from the model using a beam search. For each of those grasps, a second observation is captured that corresponds to an aligned image in the plane of the potential grasp. A grasp scoring model maps each aligned image to a score. The grasp with the highest score is selected for execution on the robot.

The model architecture used for our experiments is outlined in Figure 2. The model consists of two separate neural networks – a grasp planning module and a grasp evaluation model . The grasp planning module is used to sample grasps that are likely to be successful. The grasp evaluation model takes advantage of more detailed data in the form of a close-up image from a camera on the gripper of the robot to form a more accurate estimate of the likelihood of each sampled grasp to be successful.

The image representation is formed by passing each image through a separate convolutional neural network. The flattened output of these convolutional layers are stacked and passed through several dense layers to produce .

The neural network models a probability distribution over possible grasps for the object that corresponds to the normalized probability of success of each grasp. The model consists of submodules where is the dimensionality of the grasp. For any grasp , and are related by

where are the dimensions of .

Each is a small neural network taking the concatenation of and as input and producing a distribution over possible values for , the next dimension of . Here is a (possibly trivial) embedding of the previous grasp dimensions. For our experiments, we discretized the possible grasps . The output of is a softmax over the possible discrete values of . We found that using a small fully connected neural network for and not sharing weights between the helps improve performance at convergence.

The grasp evaluation model takes as input a single observation from the hand camera of the robot and outputs a single scalar value corresponding to the likelihood of success of that grasp. The model is parameterized by a convolutional neural network with sigmoid output.

Iii-C Training methodology

Since our method involves capturing depth images from the hand of the robot corresponding to samples from , the entire evaluation procedure is not differentiable and we cannot train the model end-to-end using supervised learning. As a result, our training procedure involves independently training and .

Given datasets of objects , observations and successful grasps , can be optimized by minimizing with respect to the parameters of the negative log-likelihood of conditioned on the observations , which is

This can be decomposed as

This function can be optimized using standard backpropogation and stochastic minibatch gradient techniques. [19]

In practice, is usually a larger model than and there are often tens or hundreds of successful grasp attempts for a single set of images . Therefore it is computationally advantageous to perform the forward pass and gradient calculations for each once for all . This can be achieved in standard neural network and backpropagation libraries by stacking all grasps for a given object so that SGD batches consist of pairs where is the matrix consisting of all successful grasps for object . To deal with differing values for , we choose and form an matrix by padding the matrix with arbitrary values. We can then write the gradient of our objective function as follows:

where is an indicator function corresponding to whether the entry in was one of the successful grasps.

The grasp evaluation function is trained using supervised learning. Inputs are given by the hand camera images collected during the data collection process and labels are given by whether the corresponding grasp was successful.

Iv Experiments

We evaluated our approach by training grasping models on three datasets:

  • ShapeNet-1M, a dataset of 1 Million scenes with a single object from the ShapeNet dataset with randomized orientation and object scale.

  • Random-1M, a dataset of 1 Million scenes with a single object generated at random using the procedure above.

  • ShapeNet-Random-1M, a dataset with 500,000 scenes from each of the previous datasets.

For each object set, we recorded 2,000 grasp attempts per object. This number of grasps was selected so that more than 95% of objects sampled had at least one successful grasp to avoid biasing the dataset to easier objects222This number is not closer to 100% because a small percentage of the random objects in our training set are un-graspable with the Fetch gripper. . For data generation, we used a disembodied Fetch gripper to improve execution speed.

We trained the models using the Adam optimizer [16] with a learning rate of . We trained each model using three random seeds, and report the average of the three seeds unless otherwise noted.

We evaluated the performance of the model on scenes from each of the following evaluation sets:

  • ShapeNet-1M

  • Random-1M

  • A held out set of ShapeNet objects

  • A held out set of randomly generated objects

  • The YCB objects with meshes that are capable of being grasped by our robot’s gripper. We sample four poses (i.e., object rotations) for each.

All executions were done using a Fetch robot in the MuJoCo physics simulator. When evaluating the model, we sampled likely grasps from the model using a beam search with beam width .

Iv-a Performance of the algorithm

Figure 3 describes the performance of the algorithm on previously seen and unseen data. The full version of our algorithm is able to achieve greater than 90% success rate on previously unseen YCB objects even when training entirely on randomly generated objects. Training on 1M random objects performs comparably to training on 1M instances of realistic objects.

ShapeNet ShapeNet Random Random
Training set Train Test Train Test Ycb
ShapeNet-1M 0.91 0.91 0.72 0.71 0.93
Random-1M 0.91 0.89 0.86 0.84 0.92
ShapeNet-Random-1M 0.92 0.90 0.84 0.81 0.92
Fig. 3: Performance of the algorithm on different synthetic test sets. The full algorithm is able to achieve at least 90% success on previously unseen objects from the YCB dataset when trained on any of the three training sets.

Note that the reported experiments were achieved by limiting the robot to grasps in which the gripper is upright. When the model attempts to predict the full 6-dimensional grasp for the object (i.e., it can change the pitch and yaw of the gripper in addition to the grasp position and the roll of the gripper), success rate is around 10% lower across experiments. We hypothesize that that this is due to the fact that (a) nearly all objects in the training and test sets can be grasped with an upright grasp, and (b) sampling a fixed number of times from a six-dimensional grasp distribution provides a much sparser estimate of than from a four-dimensional distribution (upright grasps). Further experimentation could look into whether significantly scaling the amount of training data or using a combination of the 4-dimensional training data and 6-dimensional training data could improve performance.

Figure 4 reports the precision of our algorithm on the same training and test sets. By setting a threshold of 50% on the value produced by the grasp evaluator , we can choose not to attempt a grasp if the model is not confident a successful one has been found. Here we see a more meaningful advantage for the models trained on realistic objects due to more conservative estimates by the model .

ShapeNet ShapeNet Random Random
Training set Train Test Train Test Ycb
ShapeNet-1M 0.94 0.93 0.78 0.81 0.96
(0.98) (0.98) (0.83) (0.85) (0.97)
Random-1M 0.92 0.89 0.88 0.85 0.93
(0.99) (1.00) (0.96) (0.95) (0.99)
ShapeNet- 0.92 0.91 0.87 0.85 0.94
Random-1M (1.00) (1.00) (0.97) (0.94) (0.98)
Fig. 4: Precision of the algorithm on different training and test sets. We report the success rate of grasps that were given at least 50% confidence by the model . The number in parentheses represents the fraction of objects that had at least one grasp with confidence more than 50%.

Figure 5 compares the performance of our algorithm to several baselines. In particular, our full method performs significantly better than sampling the highest likelihood grasp from the autoregressive model alone.

ShapeNet ShapeNet Random Random
Training set Train Test Train Test Ycb
Full Algorithm 0.91 0.89 0.86 0.84 0.92
Autoregressive-Only 0.89 0.86 0.80 0.76 0.89
Random 0.22 0.21 0.10 0.11 0.26
Centroid 0.30 0.25 0.10 0.12 0.54
Fig. 5: Performance of the algorithm compared to baseline approaches. The Full Algorithm and Autoregressive-Only numbers reported are using models trained on random data. The Autoregressive-Only baseline uses the model to sample a single high-likelihood grasp, and executes that grasp directly without evaluating it with the model . Grasps are sampled using a beam search with beam width 20. The Random baseline samples a random grasp. The centroid baseline deterministically attempts to grasps the center of mass of the object, with the approach angle sampled randomly.

Figure 6 shows the percentage of objects for which the top most likely grasps according to contain at least one successful grasp. The incremental likelihood of sampling a valid grasp saturates between and attempts, motivating our choice of for . Note that more objects have successful grasps among the 20 sampled than achieve success using our method, suggesting the performance of the grasp evaluator could be a bottleneck to the overall performance of the algorithm.

Fig. 6: Percentage of objects that a trained model can grasp successfully as a function of number of the number of samples from the autoregressive model attempted. Here, we sample the 20 grasps from the autoregressive model that have the highest likelihood according to a beam search and count the number of times success occurred on the th attempt for .

Iv-B Failure cases

Fig. 7: Examples of three observed failure cases for our learned models. Left: the model chooses a grasp for an object that is close to the maximum size of the gripper that collides with the object. Middle: the model chooses a grasp for a large, highly curved object that causes it to slip off. Right: the model fails to find a promising grasp for a highly irregular object.

We observed the following three main failure cases for the learned model:

  1. For objects that are close to the maximum size graspable by the gripper (10cm), the grasp chosen sometimes collides with the object.

  2. In some cases, the model chooses to grasp a curved object at a narrower point to avoid wider points that may cause collision, and as a result the gripper slips off.

  3. The model cannot find reasonable grasp candidates for some highly irregular objects like a chain found in the YCB dataset.

The learned models primarily failed on objects for which all features are close in size to the maximum allowed by the gripper. We hypothesize that the models learned to avoid wide points that would cause collision with the object, but since few objects close to the size of the gripper were present during training, this representation failed for objects with graspable features that are slightly too wide or slightly too narrow. Supplementing the training set with additional objects on the edge of graspability or combining planning with visual servoing could alleviate these edge cases.

The model also failed for a small number of highly irregular objects like a chain present in the YCB dataset. These failure cases may present a larger challenge for the use of random objects in training grasping models, but additional diversity in the random generation pipeline may mitigate the issue.

Iv-C Effect of amount of training data

Figure 8 shows the impact of the number of unique objects in the training set on the performance of our models in validation data held out from the same distribution and out-of-sample test data from the YCB dataset. Although with enough data the model trained entirely using randomly generated data performs as well as the models trained using realistic data, with smaller training sets the more realistic object distributions perform significantly better on the test set than does the unrealistic random object distribution.

Note that performance does not appear to have saturated yet in these examples. We conjecture that more training data and more training data diversity could help reduce the effects of the first two edge cases above, but may not allow the model to overcome the third edge case.

Fig. 8: Impact of number of unique training objects used on the performance of the learned model on held out data at test time. Each line represents a different training set. The top chart indicates the performance of the models on held out data from the same distribution (i.e., held out ShapeNet or Random objects depending on the training set), and the bottom chart shows performance on out-of-sample objects from the ShapeNet dataset.

V Conclusion

We demonstrated that a grasping model trained entirely using non-realistic procedurally generated objects can achieve a high success rate on realistic objects despite no training on a realistic object distribution. Our grasping model architecture allows for efficient sampling of high-likelihood grasps at evaluation time, with a successful grasp being found for 96% of objects in the first 20 samples. By scoring those samples, we can achieve a success rate of 92% on realistic objects.

Future directions that could improve the success rate of the trained models include scaling up to larger training sets, providing the model with feedback from failed grasps to influence further grasp selection, combining our grasp planning module with work on visual servoing for grasping, and incorporating additional sensor modalities like haptic feedback.

Another exciting direction is to explore using domain randomization for generalization in other robotic tasks. If realistic object models are not needed, tasks like pick-and-place, grasping in clutter, and tool use may benefit from the ability to randomly generate hundreds of thousands or millions of 3D scenes.


We thank Lukas Biewald and Rocky Duan for helpful discussions, brainstorming, and support in starting this project. The project could not have happened without the help of Peter Welinder, Jonas Schneider, Rachel Fong, and the rest of the engineering team at OpenAI.


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