Stanford Robotics and Autonomous Systems Seminar series hosts both invited and internal speakers. The seminar aims to bring the campus-wide robotics community together and provide a platform to overview and foster discussion about the progress and challenges in the various disciplines of Robotics. This quarter, the seminar is also offered to students as a 1 unit course. Note that registration to the class is NOT required in order to attend the talks.

The seminar is open to Stanford faculty, students, and sponsors.

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Schedule Fall 2019

Date Guest Affiliation Title Location Time
Fri, Sep 27 Jaime Fisac Princeton University Mind the Gap: Bridging model-based and data-driven reasoning for safe human-centered robotics Skilling Auditorium 11:00AM
Abstract

Spurred by recent advances in perception and decision-making, robotic technologies are undergoing a historic expansion from factory floors to the public space. From autonomous driving and drone delivery to robotic devices in the home and workplace, robots are bound to play an increasingly central role in our everyday lives. However, the safe deployment of these systems in complex, human-populated spaces introduces new fundamental challenges. Whether safety-critical failures (e.g. collisions) can be avoided will depend not only on the decisions of the autonomous system, but also on the actions of human beings around it. Given the complexity of human behavior, how can robots reason through these interactions reliably enough to ensure safe operation in our homes and cities? In this talk I will present a vision for safe human-centered robotics that brings together control-theoretic safety analysis and Bayesian machine learning, enabling robots to actively monitor the “reality gap” between their models and the world while leveraging existing structure to ensure safety in spite of this gap. In particular, I will focus on how robots can reason game-theoretically about the mutual influence between their decisions and those of humans over time, strategically steering interaction towards safe outcomes despite the inevitably limited accuracy of human behavioral models. I will show some experimental results on quadrotor navigation around human pedestrians and simulation studies on autonomous driving. I will end with a broader look at the pressing need for assurances in human-centered intelligent systems beyond robotics, and how control-theoretic safety analysis can be incorporated into modern artificial intelligence, enabling strong synergies between learning and safety.

Fri, Oct 04 Monroe Kennedy Stanford University Modeling and Control for Robotic Assistants Skilling Auditorium 11:00AM
Abstract

As advances are made in robotic hardware, the capacity of the complexity of tasks they are capable of performing also increases. One goal of modern robotics is to introduce robotic platforms that require very little augmentation of their environments to be effective and robust. Therefore the challenge for the Roboticist is to develop algorithms and control strategies that leverage the knowledge of the task while retaining the ability to be adaptive, adjusting to perturbations in the environment and task assumptions. These strategies will be discussed in the context of a wet-lab robotic assistant. Motivated by collaborations with a local pharmaceutical company, we will explore two relevant tasks. First, we will discuss a robot-assisted rapid experiment preparation system for research and development scientists. Second, we will discuss ongoing work for intelligent human-robot cooperative transport with limited communication. These tasks are the beginning of a suite of abilities for an assisting robotic platform that can be transferred to similar applications useful to a diverse set of end-users.

Fri, Oct 11 Adrien Gaidon Toyota Research Institute Self-Supervised Pseudo-Lidar Networks Skilling Auditorium 11:00AM
Abstract

Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception, especially in safety critical contexts like Automated Driving. Nonetheless, recent progress in combining deep learning and geometry suggests that cameras may become a competitive source of reliable 3D information. In this talk, we will present our latest developments in self-supervised monocular depth and pose estimation for urban environments. Particularly, we show that with the proper network architecture, large-scale training, and computational power it is possible to outperform fully supervised methods while still operating on the much more challenging self-supervised setting, where the only source of input information are video sequences. Furthermore, we discuss how other sources of information (i.e. camera velocity, sparse LiDAR data, and semantic predictions) can be leveraged at training time to further improve pseudo-lidar accuracy and overcome some of the inherent limitations of self-supervised learning.

Fri, Oct 18 Kostas Alexis University of Nevada Reno Field-hardened Robotic Autonomy Skilling Auditorium 11:00AM
Abstract

This talk will present our contributions in the domain of field-hardened resilient robotic autonomy and specifically on multi-modal sensing-degraded GPS-denied localization and mapping, informative path planning, and robust control to facilitate reliable access, exploration, mapping and search of challenging environments such as subterranean settings. The presented work will, among others, emphasize on fundamental developments taking place in the framework of the DARPA Subterranean Challenge and the research of the CERBERUS (https://www.subt-cerberus.org/) team, alongside work on nuclear site characterization and infrastructure inspection. Relevant field results from both active and abandoned underground mines as well as tunnels in the U.S. and in Switzerland will be presented. In addition, a selected set of prior works on long-term autonomy, including the world-record on unmanned aircraft endurance will be briefly overviewed. The talk will conclude with directions for future research to enable advanced autonomy and resilience, alongside the necessary connection to education and the potential for major broader impacts to the benefit of our economy and society.

Fri, Oct 25 Francesco Borrelli UC Berkeley Learning and Predictions in Autonomous Systems Skilling Auditorium 11:00AM
Abstract

Forecasts play an important role in autonomous and automated systems. Applications include transportation, energy, manufacturing and healthcare systems. Predictions of systems dynamics, human behavior and environment conditions can improve safety and performance of the resulting system. However, constraint satisfaction, performance guarantees and real-time computation are challenged by the growing complexity of the engineered system, the human/machine interaction and the uncertainty of the environment where the system operates. Our research over the past years has focused on predictive control design for autonomous systems performing iterative tasks. In this talk I will first provide an overview of the theory and tools that we have developed for the systematic design of learning predictive controllers. Then, I will focus on recent results on the use of data to efficiently formulate stochastic MPC problems which autonomously improve performance in iterative tasks. Throughout the talk I will focus on autonomous cars and solar power plants to motivate our research and show the benefits of the proposed techniques.

Fri, Nov 01 Tianshi Gao and Sam Abrahams Cruise Automation Scaled Learning for Autonomous Vehicles Skilling Auditorium 11:00AM
Abstract

The adoption of machine learning to solve problems in autonomous systems has become increasingly prevalent. Cruise is a developer of self-driving cars, currently operating a research and development fleet of over 100 all-electric autonomous vehicles in San Francisco. In this talk, we focus on the challenges involved with developing machine learning solutions in the autonomous driving domain. In addition to sharing lessons learned over the past few years of autonomous vehicle development, this discussion will include a review of some of the more challenging perception and prediction problems faced when operating driverless vehicles on the chaotic streets of San Francisco. Then, we share and highlight what it takes to make machine learning work in the wilderness at scale to meet these challenges.

Fri, Nov 08 Ricardo Sanfelice UC Santa Cruz Model Predictive Control of Hybrid Dynamical Systems Skilling Auditorium 11:00AM
Abstract

Hybrid systems model the behavior of dynamical systems in which the states can evolve continuously and, at isolate time instances, exhibit instantaneous jumps. Such systems arise when control algorithms that involve digital devices are applied to continuous-time systems, or when the intrinsic dynamics of the system itself has such hybrid behavior, for example, in mechanical systems with impacts, switching electrical circuits, spiking neurons, atc. Hybrid control may be used for improved performance and robustness properties compared to conventional control, and hybrid dynamics may be unavoidable due to the interplay between digital and analog components in a cyber-physical system. In this talk, we will introduce analysis and design tools for model predictive control (MPC) schemes for hybrid systems. We will present recently developed results on asymptotically stabilizing MPC for hybrid systems based on control Lyapunov functions. After a short overview of the state of the art on hybrid MPC, and a brief introduction to a powerful hybrid systems framework, we will present key concepts and analysis tools. After that, we will lay out the theoretical foundations of a general MPC framework for hybrid systems, with guaranteed stability and feasibility. In particular, we will characterize invariance properties of the feasible set and the terminal constraint sets, continuity of the value function, and use these results to establish asymptotic stability of the hybrid closed-loop system. To conclude, we will illustrate the framework in several applications and summarize some of the open problems, in particular, those related to computational issues.

Fri, Nov 15 BARS 2019 UC Berkeley and Stanford Bay Area Robotics Symposium International House 8:30AM
Abstract

The 2019 Bay Area Robotics Symposium aims to bring together roboticists from the Bay Area. The program will consist of a mix of faculty, student and industry presentations.

Fri, Nov 22 Hannah Stuart UC Berkeley TBA Skilling Auditorium 11:00AM
Abstract

TBA

Fri, Dec 06 Chelsea Finn Stanford University TBA Skilling Auditorium 11:00AM
Abstract

TBA

Sponsors

The Stanford Robotics and Autonomous Systems Seminar enjoys the support of the following sponsors.