Derivatives play a central role in optimization and machine learning. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern machine learning, making it feasible to use gradient-based optimizers to train very complex models. But are derivatives all we need? By themselves, derivatives only tell us how a function behaves on an infinitesimal scale. To use derivatives effectively, we often need to know more than that.

Reinforcement learning (RL) can enable robots to learn complex behaviors through trial-and-error interaction, getting better and better over time. Several of our prior works explored how RL can enable intricate robotic skills, such as robotic grasping, multi-task learning, and even playing table tennis. Although robotic RL has come a long way, we still don't see RL-enabled robots in everyday settings. The real world is complex, diverse, and changes over time, presenting a major challenge for robotic systems. However, we believe that RL should offer us an excellent tool for tackling precisely these challenges: by continually practicing, getting better, and learning on the job, robots should be able to adapt to the world as it changes around them.

Aging is a process that is characterized by physiological and molecular changes that increase an individual's risk of developing diseases and eventually dying. Being able to measure and estimate the biological signatures of aging can help researchers identify preventive measures to reduce disease risk and impact. Researchers have developed "aging clocks" based on markers such as blood proteins or DNA methylation to measure individuals' biological age, which is distinct from one's chronological age. These aging clocks help predict the risk of age-related diseases. But because protein and methylation markers require a blood draw, non-invasive ways to find similar measures could make aging information more accessible.

Bayesian optimization (BayesOpt) is a powerful tool widely used for global optimization tasks, such as hyperparameter tuning, protein engineering, synthetic chemistry, robot learning, and even baking cookies. BayesOpt is a great strategy for these problems because they all involve optimizing black-box functions that are expensive to evaluate. A black-box function's underlying mapping from inputs (configurations of the thing we want to optimize) to outputs (a measure of performance) is unknown. However, we can attempt to understand its internal workings by evaluating the function for different combinations of inputs. Because each evaluation can be computationally expensive, we need to find the best inputs in as few evaluations as possible.

People are excellent navigators of the physical world, due in part to their remarkable ability to build cognitive maps that form the basis of spatial memory -- from localizing landmarks at varying ontological levels (like a book on a shelf in the living room) to determining whether a layout permits navigation from point A to point B. Building robots that are proficient at navigation requires an interconnected understanding of (a) vision and natural language (to associate landmarks or follow instructions), and (b) spatial reasoning (to connect a map representing an environment to the true spatial distribution of objects). While there have been many recent advances in training joint visual-language models on Internet-scale data, figuring out how to best connect them to a spatial representation of the physical world that can be used by robots remains an open research question. To explore this, we collaborated with researchers at the University of Freiburg and Nuremberg to develop Visual Language Maps (VLMaps), a map representation that directly fuses pre-trained visual-language embeddings into a 3D reconstruction of the environment. VLMaps, which is set to appear at ICRA 2023, is a simple approach that allows robots to (1) index visual landmarks in the map using natural language descriptions, (2) employ Code as Policies to navigate to spatial goals, such as "go in between the sofa and TV" or "move three meters to the right of the chair", and (3) generate open-vocabulary obstacle maps -- allowing multiple robots with different morphologies (mobile manipulators vs. drones, for example) to use the same VLMap for path planning. VLMaps can be used out-of-the-box without additional labeled data or model fine-tuning, and outperforms other zero-shot methods by over 17% on challenging object-goal and spatial-goal navigation tasks in Habitat and Matterport3D.

Despite decades of research, we don't see many mobile robots roaming our homes, offices, and streets. Real-world robot navigation in human-centric environments remains an unsolved problem. These challenging situations require safe and efficient navigation through tight spaces, such as squeezing between coffee tables and couches, maneuvering in tight corners, doorways, untidy rooms, and more. An equally critical requirement is to navigate in a manner that complies with unwritten social norms around people, for example, yielding at blind corners or staying at a comfortable distance. Google Research is committed to examining how advances in ML may enable us to overcome these obstacles.

Google's focus on AI stems from the conviction that this transformational technology will benefit society through its capacity to assist, complement, and empower people in almost every field and sector. In no area is the magnitude of this opportunity greater than in the spheres of healthcare and medicine. Commensurate with our mission to demonstrate these societal benefits, Google Research's programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022. Our Health research publications have had broad impact, spanning the fields of biomarkers, consumer sensors, dermatology, endoscopy, epidemiology, medicine, genomics, oncology, ophthalmology, pathology, public & environmental health, and radiology. In each section, we emphasize the importance of a measured and collaborative approach to innovation in health. Unlike the "launch and iterate" approach typical in consumer product development, applying ML to health requires thoughtful assessment, ecosystem awareness, and rigorous testing.

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. In the last 10 years, AI and ML models have become bigger and more sophisticated -- they're deeper, more complex, with more parameters, and trained on much more data, resulting in some of the most transformative outcomes in the history of machine learning. As these models increasingly find themselves deployed in production and business applications, the efficiency and costs of these models has gone from a minor consideration to a primary constraint. In response, Google has continued to invest heavily in ML efficiency, taking on the biggest challenges in (a) efficient architectures, (b) training efficiency, (c) data efficiency, and (d) inference efficiency. Beyond efficiency, there are a number of other challenges around factuality, security, privacy and freshness in these models.

Major recent advances in multiple subfields of machine learning (ML) research, such as computer vision and natural language processing, have been enabled by a shared common approach that leverages large, diverse datasets and expressive models that can absorb all of the data effectively. Although there have been various attempts to apply this approach to robotics, robots have not yet leveraged highly-capable models as well as other subfields. Several factors contribute to this challenge. First, there's the lack of large-scale and diverse robotic data, which limits a model's ability to absorb a broad set of robotic experiences. Data collection is particularly expensive and challenging for robotics because dataset curation requires engineering-heavy autonomous operation, or demonstrations collected using human teleoperations. To address these challenges, we propose the Robotics Transformer 1 (RT-1), a multi-task model that tokenizes robot inputs and outputs actions (e.g., camera images, task instructions, and motor commands) to enable efficient inference at runtime, which makes real-time control feasible.

In recent years, language models (LMs) have become more prominent in natural language processing (NLP) research and are also becoming increasingly impactful in practice. Scaling up LMs has been shown to improve performance across a range of NLP tasks. For instance, scaling up language models can improve perplexity across seven orders of magnitude of model sizes, and new abilities such as multi-step reasoning have been observed to arise as a result of model scale. However, one of the challenges of continued scaling is that training new, larger models requires great amounts of computational resources. Moreover, new models are often trained from scratch and do not leverage the weights from previously existing models.