Robots that Learn to Use Improvised Tools


In many animals, tool-use skills emerge from a combination of observational learning and experimentation. For example, by watching one another, chimpanzees can learn how to use twigs to “fish” for insects. Similarly, capuchin monkeys demonstrate the ability to wield sticks as sweeping tools to pull food closer to themselves. While one might wonder whether these are just illustrations of “monkey see, monkey do,” we believe these tool-use abilities indicate a greater level of intelligence.

Left: A chimpanzee fishing for termites. Right: A gorilla using a stick to gather herbs. (source)

The question our new work explores is: can we enable robots to use tools in the same way — through observation and experimentation?

A requisite for performing complex multi-object manipulation tasks, such as those involved in tool use, is an understanding of physical cause-and-effect relationships. Therefore, the ability to predict how one object might interact with another is crucial. Our prior work has investigated how visual predictive models of cause-and-effect can be learned from unsupervised robot interaction with the world. After learning such a model, the robot can plan to accomplish a diverse set of simple tasks, including cloth folding and object arrangement. However, if we consider the more complex interactions that occur in tool-use tasks, such as how a broom can sweep dirt into a dustpan, undirected experimentation isn’t enough.

Hence, taking inspiration from how animals learn, we designed an algorithm that allows robots to learn tool-use skills through a similar paradigm of imitation and interaction. In particular, we show that, with a mix of demonstration data and unsupervised experience, a robot can use novel objects as tools and even improvise tools in the absence of traditional ones. Further, depending on the demands of the task, our method demonstrates the ability to decide whether to use the provided tools. In this post, we will describe how this works.


CVPR 2019 Challenges on Domain Adaptation in Autonomous Driving


We all dream of a future in which autonomous cars can drive us to every corner of the world. Numerous researchers and companies are working day and night to chase this dream by overcoming scientific and technological barriers. One of the greatest challenges we still face is developing machine learning models that can be trained in a local environment and also perform well in new, unseen situations. For example, self-driving cars may utilize perception models to recognize drivable areas from images. Companies in Silicon Valley can build and perfect such a model using large local datasets from the Bay Area for training. However, if the same model were deployed in a snowy area such as Boston, it would likely perform miserably, because it has never seen snow before. Boston, during winter, and Silicon Valley, during any time of the year, can be labeled as separate domains for perception models, since they present clear differences in climate and challenges in perception. In other cases, domains may be much closer in nature, such as a city street and a nearby highway. The process of transferring knowledge and models between different domains in machine learning is called domain adaptation.

A large number of papers on domain adaptation of perception models have appeared in top publishing venues for machine learning and computer vision. However, most of these works focus on image classification and semantic segmentation. Hardly any attention has been paid to instance-level tasks, such as object detection and tracking, even though localization of nearby objects is arguably more important for autonomous driving. To foster the study of domain adaptation of perception models, Berkeley DeepDrive and Didi Chuxing are co-hosting two competitions in CVPR 2019 Workshop on Autonomous Driving. The challenges will focus on domain adaptation of object detection and tracking based on the BDD100K, from Berkeley DeepDrive, and D2-City, from Didi Chuxing, datasets. The domain of BDD100K covers US scenes, while D2-City was collected on China’s streets. The competitions ask participants to transfer object detectors from BDD100K to D2-City and object trackers from D2-city to BDD100K. More information about the challenges can be found on our website and D2-City.

Following our introduction of the BDD100K dataset, we have been busy working to provide more temporal annotations. Above is an example of object tracking annotation, created by our open-source annotation platform Scalabel. Some of the tracking labels are used in the domain adaptation challenge for object tracking. More data will be released this summer. Of course, we also have object tracking at night.


Announcing the BAIR Open Research Commons


The University of California Berkeley Artificial Intelligence Research (BAIR) Lab is pleased to announce the BAIR Open Research Commons, a new industrial affiliate program launched to accelerate cutting-edge AI research. AI research is advancing rapidly in both university and corporate research settings, with existing collaborations already underway driven by individual researcher-to-researcher collaborations. The BAIR Commons is designed to enhance and streamline such collaborative cutting-edge research by students, faculty, and corporate research scholars.

The Commons agreement has been framed with the goal of promoting open research in AI: all on-campus effort, data, and results in the Commons program will be non-exclusive with open publication and open-source code release expected. Fostering an environment for excellence for graduate student research is the primary motivation of the new program: Berkeley students will lead the design of projects in the Commons, and the program of research must be approved by their home departments before a project commences. Students are expected to benefit from collaboration with leading researchers in industrial research labs, as well as the availability of partner resources useful to investigate certain open questions in state-of-the-art AI research. The University will benefit from membership fees paid by partners to participate in the program. The Commons agreement provides for collaborative joint projects between the partners and Berkeley, with intellectual property shared jointly and equally by the parties.

The agreement also provides for joint research “lablets”, which will be embedded collaborative open research spaces inside BAIR’s 27,000 sq. ft. research facility opening this summer in the Berkeley Way West facility on the Berkeley campus. More than a dozen faculty and 120 students will be assigned space in the new lab, with an equal number of visiting positions allocated for researchers from other BAIR labs and for visiting industrial partners.

Initial alliance participants include Amazon, Facebook, Google, Samsung, and Wave Computing. Funding for over twenty joint projects has been committed in the initial launch of the program, which will support both BAIR facilities and research efforts. Over 30 faculty and 200 graduate students and postdocs at Berkeley are affiliated with BAIR. For more information about BAIR or the Commons program please contact

BAIR will occupy the top floor of Berkeley Way West.


Manipulation By Feel


Guiding our fingers while typing, enabling us to nimbly strike a matchstick, and inserting a key in a keyhole all rely on our sense of touch. It has been shown that the sense of touch is very important for dexterous manipulation in humans. Similarly, for many robotic manipulation tasks, vision alone may not be sufficient – often, it may be difficult to resolve subtle details such as the exact position of an edge, shear forces or surface textures at points of contact, and robotic arms and fingers can block the line of sight between a camera and its quarry. Augmenting robots with this crucial sense, however, remains a challenging task.

Our goal is to provide a framework for learning how to perform tactile servoing, which means precisely relocating an object based on tactile information. To provide our robot with tactile feedback, we utilize a custom-built tactile sensor, based on similar principles as the GelSight sensor developed at MIT. The sensor is composed of a deformable, elastomer-based gel, backlit by three colored LEDs, and provides high-resolution RGB images of contact at the gel surface. Compared to other sensors, this tactile sensor sensor naturally provides geometric information in the form of rich visual information from which attributes such as force can be inferred. Previous work using similar sensors has leveraged the this kind of tactile sensor on tasks such as learning how to grasp, improving success rates when grasping a variety of objects.


Assessing Generalization in Deep Reinforcement Learning



We present a benchmark for studying generalization in deep reinforcement learning (RL). Systematic empirical evaluation shows that vanilla deep RL algorithms generalize better than specialized deep RL algorithms designed specifically for generalization. In other words, simply training on varied environments is so far the most effective strategy for generalization. The code can be found at and the full paper is at


Controlling False Discoveries in Large-Scale Experimentation: Challenges and Solutions


“Scientific research has changed the world. Now it needs to change itself.”

- The Economist, 2013

There has been a growing concern about the validity of scientific findings. A multitude of journals, papers and reports have recognized the ever smaller number of replicable scientific studies. In 2016, one of the giants of scientific publishing, Nature, surveyed about 1,500 researchers across many different disciplines, asking for their stand on the status of reproducibility in their area of research. One of the many takeaways to the worrisome results of this survey is the following: 90% of the respondents agreed that there is a reproducibility crisis, and the overall top answer to boosting reproducibility was “better understanding of statistics”. Indeed, many factors contributing to the explosion of irreproducible research stem from the neglect of the fact that statistics is no longer as static as it was in the first half of the 20th century, when statistical hypothesis testing came into prominence as a theoretically rigorous proposal for making valid discoveries with high confidence.


Learning Preferences by Looking at the World


It would be great if we could all have household robots do our chores for us. Chores are tasks that we want done to make our houses cater more to our preferences; they are a way in which we want our house to be different from the way it currently is. However, most “different” states are not very desirable:

Surely our robot wouldn’t be so dumb as to go around breaking stuff when we ask it to clean our house? Unfortunately, AI systems trained with reinforcement learning only optimize features specified in the reward function and are indifferent to anything we might’ve inadvertently left out. Generally, it is easy to get the reward wrong by forgetting to include preferences for things that should stay the same, since we are so used to having these preferences satisfied, and there are so many of them. Consider the room below, and imagine that we want a robot waiter that serves people at the dining table efficiently. We might implement this using a reward function that provides 1 reward whenever the robot serves a dish, and use discounting so that the robot is incentivized to be efficient. What could go wrong with such a reward function? How would we need to modify the reward function to take this into account? Take a minute to think about it.


Soft Actor Critic—Deep Reinforcement Learning with Real-World Robots


We are announcing the release of our state-of-the-art off-policy model-free reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has been developed jointly at UC Berkeley and Google, and we have been using it internally for our robotics experiment. Soft actor-critic is, to our knowledge, one of the most efficient model-free algorithms available today, making it especially well-suited for real-world robotic learning. In this post, we will benchmark SAC against state-of-the-art model-free RL algorithms and showcase a spectrum of real-world robot examples, ranging from manipulation to locomotion. We also release our implementation of SAC, which is particularly designed for real-world robotic systems.


Scaling Multi-Agent Reinforcement Learning


An earlier version of this post is on the RISELab blog. It is posted here with the permission of the authors.

We just rolled out general support for multi-agent reinforcement learning in Ray RLlib 0.6.0. This blog post is a brief tutorial on multi-agent RL and how we designed for it in RLlib. Our goal is to enable multi-agent RL across a range of use cases, from leveraging existing single-agent algorithms to training with custom algorithms at large scale.


Building Gene Expression Atlases with Deep Generative Models for Single-cell Transcriptomics


Figure: An artistic representation of single-cell RNA sequencing. The stars in the sky represent cells in a heterogeneous tissue. The projection of the stars onto the river reveals relationships among them that are not apparent by looking directly at the sky. Like the river, our Bayesian model, called scVI, reveals relationships among cells.

The diversity of gene regulatory states in our body is one of the main reasons why such an amazing array of biological functions can be encoded in a single genome. Recent advances in microfluidics and sequencing technologies (such as inDrops) enabled measurement of gene expression at the single-cell level and has provided tremendous opportunities to unravel the underlying mechanisms of relationships between individual genes and specific biological phenomena. These experiments yield approximate measurements for mRNA counts of the entire transcriptome (i.e around $d = 20,000$ protein-coding genes) and a large number of cells $n$, which can vary from tens of thousands to a million cells. The early computational methods to interpret this data relied on linear model and empirical Bayes shrinkage approaches due to initially extremely low sample-size. While current research focuses on providing more accurate models for this gene expression data, most of the subsequent algorithms either exhibit prohibitive scalability issues or remain limited to a unique downstream analysis task. Consequently, common practices in the field still rely on ad-hoc preprocessing pipelines and specific algorithmic procedures, which limits the capabilities of capturing the underlying data generating process.

In this post, we propose to build up on the increased sample-size and recent developments in Bayesian approximate inference to improve modeling complexity as well as algorithmic scalability. Notably, we present our recent work on deep generative models for single-cell transcriptomics, which addresses all the mentioned limitations by formalizing biological questions into statistical queries over a unique graphical model, tailored to single-cell RNA sequencing (scRNA-seq) datasets. The resulting algorithmic inference procedure, which we named Single-cell Variational Inference (scVI), is open-source and scales to over a million cells.