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How Lantao Liu and his team are helping robots adapt to challenges

Published

4 years ago

November 29, 2021

Lantao Liu and his team at the Vehicle Autonomy and Intelligence Lab (VAIL) at Indiana University Bloomington want to help robots get better at navigating through complex and sometimes changing environments, while also boosting their ability to assess and process data. This challenge has significant applications, particularly in the realm of environmental modeling. Liu and his team are working to develop autonomous and machine learning methods and open-source libraries that can potentially benefit both the artificial intelligence and robotics communities.

“Machine learning algorithms are increasingly being developed for robotics missions. Many critical autonomy components are data-driven, where the data comes from onboard sensors such as LiDAR, sonar, and cameras,” says Liu who also is an assistant professor within the university’s Department of Intelligent Systems Engineering in the Luddy School of Informatics, Computing, and Engineering.

Lantao Liu leads the Vehicle Autonomy and Intelligence Lab at Indiana University Bloomington.

Courtesy of Lantao Liu

“The robots typically have weak computational capacity due to their limited dimensions and payloads, yet they require online learning with data processed on the fly,” he adds. “Unfortunately, many methods for solving these tasks entail large computational costs that can be very challenging for the robots. The key challenges have been computational-theoretical due to the increased complexity of stochastic modeling, but also practical due to the synergy of integrating hardware and software systems as well as customizing algorithms on the robots.”

Liu’s 2019 Amazon Machine Learning Research Award allows VAIL to access and leverage Amazon’s cloud computing tools and services for thousands of hours, boosting their work on both machine learning and autonomous systems.

“My lab works on various decision-making problems for different types of robots including aerial, ground, and aquatic vehicles. Our objective is to develop methodologies for autonomous robots to enhance their autonomy and intelligence in environmental sensing and modeling, search and rescue, among other applications of societal importance,” explains Liu.

Environmental sensing, modeling, and monitoring

One project being pursued by VAIL researchers involves a process that maps environmental attributes of interest, such as pollution in the water or air, by collecting corresponding measurement samples from different locations so that a “distribution map” (environment model) can be reconstructed.

“This mapping mechanism is also called environmental state estimation, a learning process where the parameters of an underlying environment model must be learned using streams of incoming sampling data collected by robots,” Liu explains.

“However, the environments can be dynamic, as can the associated environmental attributes to be mapped. A drawback to using robots is that the collection of samples requires a series of sequential, ordered, sampling operations (so data may not well represent the ground-truth map), and the entire sampling process is time consuming because the samples are typically spread over different spatial locations.

Environmental sensing, modeling, and monitoring using autonomous surface vehicles

“To provide a good estimate of the state of the environment at any time, the robot information-gathering sensing must be persistent to keep up with evolving environmental dynamics,” Liu explains. “One focus of our research has been developing principles that use data-driven methods to guide robots to learn the spatio-temporal and stochastic environment model, and utilize the learned model for path planning and decision-making solutions. This, in turn, benefits future environmental exploration and exploitation for subsequent modeling and monitoring.”

The VAIL team has been developing methods and software that can accurately characterize the spatiotemporal environment by designing a non-stationary modeling framework based on a variant of Gaussian processes (GPs).

“The map will not be the same everywhere,” says Liu. “There are locations on the map that vary more rapidly than others, and we need to accurately model both rapidly and slowly changing parts. It is even more challenging when the underlying map is dynamic, such as when we’re mapping pollution dispersion.

“In addition,” he explains, “the model computation must be fast for in-the-moment decisions. However, sensing data is continuously received, and the accumulated data quickly overwhelms the robots’ computing resources. To boost the learning performance, our researchers recently developed an adaptive learning approach where the key idea is a sparse approximation mechanism that incrementally incorporates the new incoming data with a learned model supported by ‘summarized’ old data.”

Robotic anomaly detection

In a related project, the lab has been developing a generic robotic anomaly detection framework, motivated by field experiments.

“Commonly, robots in the field encounter sensing and behavioral anomalies,” Liu explains. “For example, one of the thrusters of the autonomous surface vehicle (ASV) might malfunction in operation, resulting in a forward motion becoming a turning motion. Or the ASV might get stuck in aquatic plants or other underwater obstacles, which are difficult to perceive using cameras or LiDARs. The inertial measurement unit (IMU) can be sensitive to external disturbances such as magnetic fields and provide drifting readings. Surrounding objects, such as a tall tree near the shore, might block the GPS signals, which leads to inaccurate localization. Sonar data can also be affected by dynamic underwater objects or environmental disturbances.

“Resilient and adaptive robotic systems require cognitive capabilities to avoid anomalies and recover and learn from failures with minimal human intervention,” Liu adds. “Equipping robots with the self-examination ability to detect sensing and behavioral faults is an essential step. The intuitive idea of anomaly detection is to develop some concept of normality and treat the observations that deviate considerably from that as anomalies.

“It is difficult, if not impossible, to handcraft a model representing the expected behaviors of different kinds of robots in various applications,” Liu explains. “The framework learns the concept of normality via deep representation learning and graph neural networks. We train the framework using contrastive learning in a semi-supervised manner that utilizes the information in a large amount of unlabeled data and, optionally, a small amount of labeled data. During the development of this framework, the AWS EC2 instances have drastically accelerated the prototyping, training, and testing processes. We are currently finalizing this framework and will open-source software.

“Hopefully,” he adds, “it will also benefit the robotics and machine learning communities at large.”

Off-road autonomy

The AWS Machine Learning Research Award also helps VAIL research off-road autonomy.

“An important challenge is the stochastic modeling of unexpected robot behaviors,” he explains. “Basically, the robots operating in real-world complex environments need to reason about the long-term results of their physical interactions with the environment, but due to the high complexity of the real world, it is generally impossible to predict future events in an accurate manner.

“For example,” says Liu, “the effect of uneven road conditions or various disturbances on the robot’s motion is hard to model (or learn from data) precisely. It is even more challenging to model the interaction between the robot and the environment, especially when the environment is dynamic. Other representative scenarios include drones flying with strong winds or submarines moving under ocean currents, where air and water flows vary significantly in both space and time.

“Thus, it is necessary for the robots to consider these epistemic uncertainties caused by a lack of precise modeling of the environment while making decisions,” he explains. “We use Markov decision process as a basis to model autonomous decision-making under uncertainty problems. The solution to these problems is a closed-loop policy that maximizes a long-term goal and satisfies the safety constraints under a probabilistic interaction model between the robot and the environment. In principle, the resulting policy can generate a sequence of motor commands that complete the task assigned by a human, given that the probabilistic model can well describe the uncertainty of the world, and the computational method can allow the robot to calculate the policy within a reasonable amount of time.

“However,” Liu continues, “many real-world problems are non-trivial, and obtaining the required probabilistic model of the world is generally impossible. Our research focuses on solving these two challenges by developing novel methods and leveraging the strong computational power of GPUs. Our current focus is on addressing the computational part of the challenge by developing two planning algorithms that allow the robot to reason about its continuous motion on complicated terrain surfaces based on the kernel method (mesh-free) and finite-element method (mesh-based). Both methods leverage a set of discrete elements to represent the value function over the continuous space. The computation over the discrete parts can be parallelized, which allows our robot to reason and compute optimal policies in real-time to navigate through complicated terrains safely and efficiently.”

VAIL researchers have been working on using sampling methods to optimize over a class of parameterized policies.

Lantao Liu and his team used AWS cloud computing services to speed up computation and analyses of robot decision-making policies in a simulated scenario.

“To do so, we first need to sample a large number of robot trajectories under the current policy, which can be computed quickly by the parallel architecture of Nvidia GPU CUDA cores,” Liu explains. “They use the gradient-based method for optimization of policy parameters: the policy is updated by computing the policy parameter gradients based on the sampled trajectories. The gradient computation and policy update involve large matrix operations, which can also be parallelized by GPUs for real-time solutions. They leverage AWS computation for this task.”

Navigable space segmentation for navigation

Liu notes that the AWS resources have also been very useful for the team’s visual autonomy research. Visual information has become increasingly important for robotic autonomy as it can provide rich information about surrounding environments, and VAIL’s visual data processing capability has been significantly improved due to the breakthrough on deep neural networks (DNNs). To develop deep approaches to process the vision perception, the team needs to develop models with complicated learning architectures, huge volumes of data, as well as various training strategies.

“A crucial capability for mobile robots to navigate in unknown environments is to construct obstacle-free space where the robot could move without collision,” Liu explains. “Roboticists have been developing methods for detecting such free space with the ray tracing of LiDAR beams to build occupancy maps in 2D or 3D space. Mapping methods with LiDAR require processing of large point cloud data, especially when a high-resolution LiDAR is used. As a much less expensive alternative, cameras have also been widely used for free space detection by leveraging DNNs to perform multi-class or binary-class segmentation of images.

Navigable space construction for robot visual navigation

“However,” he adds, “most existing DNN-based methods are built on a supervised-learning paradigm and rely on annotated datasets. The datasets usually contain a large amount of pixel-level annotated segmented images, which are prohibitively expensive and time-consuming to obtain for robotic applications in outdoor environments. To overcome limitations of fully supervised learning, we have been developing a new deep model based on variational auto-encoders. We target a representation learning-based framework to enable robots to learn navigable space segmentation in an unsupervised manner, with the aim of learning a polyline representation that compactly outlines the desired navigable space boundary. This is different from prevalent segmentation techniques which heavily rely on supervised learning strategies and typically demand immense pixel-level annotated images.

“We trained our model with the data from public datasets using GPUs,” Liu explains. “The large number of computing cores and memory space on AWS have enabled us to train our model fast and with high efficacy. This is crucial as it allows us to test and redesign models rapidly and provides great convenience to deploy the trained model to the robot systems.

“We then train our model with a small set of collected unlabeled images in real mission environments,” Liu adds. “Early testing shows that our model is able to detect navigable space in real time with high accuracy. “The computational resources provided by Amazon have greatly accelerated our design process.”

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A New Ranking Framework for Better Notification Quality on Instagram

Published

1 day ago

September 2, 2025

Xian Sun

We’re sharing how Meta is applying machine learning (ML) and diversity algorithms to improve notification quality and user experience.
We’ve introduced a diversity-aware notification ranking framework to reduce uniformity and deliver a more varied and engaging mix of notifications.
This new framework reduces the volume of notifications and drives higher engagement rates through more diverse outreach.

Notifications are one of the most powerful tools for bringing people back to Instagram and enhancing engagement. Whether it’s a friend liking your photo, another close friend posting a story, or a suggestion for a reel you might enjoy, notifications help surface moments that matter in real time.

Instagram leverages machine learning (ML) models to decide who should get a notification, when to send it, and what content to include. These models are trained to optimize for user positive engagement such as click-through-rate (CTR) – the probability of a user clicking a notification – as well as other metrics like time spent.

However, while engagement-optimized models are effective at driving interactions, there’s a risk that they might overprioritize the product types and authors someone has previously engaged with. This can lead to overexposure to the same creators or the same product types while overlooking other valuable and diverse experiences.

This means people could miss out on content that would give them a more balanced, satisfying, and enriched experience. Over time, this can make notifications feel spammy and increase the likelihood that people will disable them altogether.

The real challenge lies in finding the right balance: How can we introduce meaningful diversity into the notification experience without sacrificing the personalization and relevance people on Instagram have come to expect?

To tackle this, we’ve introduced a diversity-aware notification ranking framework that helps deliver more diverse, better curated, and less repetitive notifications. This framework has significantly reduced daily notification volume while improving CTR. It also introduces several benefits:

The extensibility of incorporating customized soft penalty (demotion) logic for each dimension, enabling more adaptive and sophisticated diversity strategies.
The flexibility of tuning demotion strength across dimensions like content, author, and product type via adjustable weights.
The integration of balancing personalization and diversity, ensuring notifications remain both relevant and varied.

The Risks of Notifications without Diversity

The issue of overexposure in notifications often shows up in two major ways:

Overexposure to the same author: People might receive notifications that are mostly about the same friend. For example, if someone often interacts with content from a particular friend, the system may continue surfacing notifications from that person alone – ignoring other friends they also engage with. This can feel repetitive and one-dimensional, reducing the overall value of notifications.

Overexposure to the same product surface: People might mostly receive notifications from the same product surface such as Stories, even when Feed or Reels could provide value. For example, someone may be interested in both reel and story notifications but has recently interacted more often with stories. Because the system heavily prioritizes past engagement, it sends only story notifications, overlooking the person’s broader interests.

Introducing Instagram’s Diversity-Aware Notification Ranking Framework

Instagram’s diversity-aware notification ranking framework is designed to enhance the notification experience by balancing the predicted potential for user engagement with the need for content diversity. This framework introduces a diversity layer on top of the existing engagement ML models, applying multiplicative penalties to the candidate scores generated by these models, as figure1, below, shows.

The diversity layer evaluates each notification candidate’s similarity to recently sent notifications across multiple dimensions such as content, author, notification type, and product surface. It then applies carefully calibrated penalties—expressed as multiplicative demotion factors—to downrank candidates that are too similar or repetitive. The adjusted scores are used to re-rank the candidates, enabling the system to select notifications that maintain high engagement potential while introducing meaningful diversity. In the end, the quality bar selects the top-ranked candidate that passes both the ranking and diversity criteria.

Figure.1: Instagram’s diversity-aware ranking framework where the diversity layer sits on top of the existing modeling layer and penalizes notifications that are too similar to recently sent ones.

Mathematical Formulation

Within the diversity layer, we apply a multiplicative demotion factor to the base relevance score of each candidate. Given a notification candidate 𝑐, we compute its final score as the product of its base ranking score and a diversity demotion multiplier:

$\text{Score}(c) = R(c) \times D(c)$

where R(c) represents the candidate’s base relevance score, and D(c) ∈ [0,1] is a penalty factor that reduces the score based on similarity to recently sent notifications. We define a set of semantic dimensions (e.g., author, product type) along which we want to promote diversity. For each dimension i, we compute a similarity signal p_i(c) between candidate c and the set of historical notifications H, using a maximal marginal relevance (MMR) approach:

$p_i(c) = \mathrm{max}_{h \in H}\mathrm{sim}_i(c, h)$

where sim_i(·,·) is a predefined similarity function for dimension i. In our baseline implementation, p_i(c) is binary: it equals 1 if the similarity exceeds a threshold 𝜏_i and 0 otherwise.

The final demotion multiplier is defined as:

$D(c) = \prod_{i=1}^{m} \left( 1 - w_i \cdot p_i(c) \right)$

where each w_i∈ [0,1] controls the strength of demotion for its respective dimension. This formulation ensures that candidates similar to previously delivered notifications along one or more dimensions are proportionally down-weighted, reducing redundancy and promoting content variation. The use of a multiplicative penalty allows for flexible control across multiple dimensions, while still preserving high-relevance candidates.

The Future of Diversity-Aware Ranking

As we continue evolving our notification diversity-aware ranking system, a next step is to introduce more adaptive, dynamic demotion strategies. Instead of relying on static rules, we plan to make demotion strength responsive to notification volume and delivery timing. For example, as a user receives more notifications—especially of similar type or in rapid succession—the system progressively applies stronger penalties to new notification candidates, effectively mitigating overwhelming experiences caused by high notification volume or tightly spaced deliveries.

Longer term, we see an opportunity to bring large language models (LLMs) into the diversity pipeline. LLMs can help us go beyond surface-level rules by understanding semantic similarity between messages and rephrasing content in more varied, user-friendly ways. This would allow us to personalize notification experiences with richer language and improved relevance while maintaining diversity across topics, tone, and timing.

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Simplifying book discovery with ML-powered visual autocomplete suggestions

Published

1 day ago

September 2, 2025

Mao Sheng Liu

Every day, millions of customers search for books in various formats (audiobooks, e-books, and physical books) across Amazon and Audible. Traditional keyword autocomplete suggestions, while helpful, usually require several steps before customers find their desired content. Audible took on the challenge of making book discovery more intuitive and personalized while reducing the number of steps to purchase.

We developed an instant visual autocomplete system that enhances the search experience across Amazon and Audible. As the user begins typing a query, our solution provides visual previews with book covers, enabling direct navigation to relevant landing pages instead of the search result page. It also delivers real-time personalized format recommendations and incorporates multiple searchable entities, such as book pages, author pages, and series pages.

1 of 2

Audible’s visual-autocomplete experience.

2 of 2

Amazon’s visual-autocomplete experience.

Our system needed to understand user intent from just a few keystrokes and determine the most relevant books to display, all while maintaining low latency for millions of queries. Using historical search data, we match keystrokes to products, transforming partial inputs into meaningful search suggestions. To ensure quality, we implemented confidence-based filtering mechanisms, which are particularly important for distinguishing between general queries like “mystery” and specific title searches. To reflect customers’ most recent interests, the system applies time-decay functions to long historical user interaction data.

Events & Conferences

Revolutionizing warehouse automation with scientific simulation

Published

1 week ago

August 26, 2025

Deniz Akyildiz

Modern warehouses rely on complex networks of sensors to enable safe and efficient operations. These sensors must detect everything from packages and containers to robots and vehicles, often in changing environments with varying lighting conditions. More important for Amazon, we need to be able to detect barcodes in an efficient way.