diff --git a/_talks/carlone_23.md b/_talks/carlone_23.md index 5888086..3a7c0a7 100644 --- a/_talks/carlone_23.md +++ b/_talks/carlone_23.md @@ -8,5 +8,5 @@ location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761& title: "Foundations of Spatial Perception for Robotics" abstract: "A large gap still separates robot and human perception: humans are able to quickly form a holistic representation of the scene that encompasses both geometric and semantic aspects, are robust to a broad range of perceptual conditions, and are able to learn without low-level supervision. This talk discusses recent efforts to bridge these gaps. First, we show that scalable metric-semantic scene understanding requires hierarchical representations; these hierarchical representations, or 3D scene graphs, are key to efficient storage and inference, and enable real-time perception algorithms. Second, we discuss progress in the design of certifiable algorithms for robust estimation, which provide first-of-a-kind performance guarantees for estimation problems arising in robot perception. Finally, we observe that certification and self-supervision are twin challenges, and the design of certifiable perception algorithms enables a natural self-supervised learning scheme; we apply this insight to 3D object pose estimation and present self-supervised algorithms that perform on par with state-of-the-art, fully supervised methods, while not requiring manual 3D annotations. " -youtube-code: "TBD" +youtube-code: "L3cdMDIJqWs" --- diff --git a/_talks/herbert_23.md b/_talks/herbert_23.md index 89a6f77..5eb1e49 100644 --- a/_talks/herbert_23.md +++ b/_talks/herbert_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18" title: "Blending Data-Driven CBF Approximations with HJ Reachability" abstract: "In this talk I will discuss recent joint work with Professor Sicun (Sean) Gao on using data-driven CBF approximations for safe control of autonomous systems. First I will discuss how we blend CBF approximations and HJ reachability for systems with modeled dynamics. The data-driven CBF approximation provides an efficient initial estimate of the true CBF, which is then refined using HJ reachability analysis. This work was presented at IROS 2022, with some new additions. Next I will discuss our recent work on how we use data-driven CBFs for hard-to-model dynamics (e.g. interaction behavior among pedestrians). Our approach exploits an important observation: the spatial interaction patterns of multiple dynamic obstacles can be decomposed and predicted through temporal sequences of states for each obstacle. Through decomposition, we can generalize control policies trained only with a small number of obstacles, to environments where the obstacle density can be 100x higher. We have no guarantees on safety (at least so far), but we empirically show significant improvements to dynamic collision avoidance (compared to other learning methods) without being overly conservative (compared to control theoretic methods). This work won the Robocup best paper award this month at IROS 2023." -youtube-code: "TBD" +youtube-code: "JL2UCAnyrjI" --- diff --git a/_talks/ivanovic_23.md b/_talks/ivanovic_23.md index d98c19a..ecdd8e0 100644 --- a/_talks/ivanovic_23.md +++ b/_talks/ivanovic_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18" title: "Architecting Next-Generation AV Autonomy Stacks" abstract: "Learning-based components are ubiquitous within modern robotic autonomy stacks. However, many of these components are not being utilized to their fullest potential, with training and evaluation schemes that are agnostic to their eventual downstream tasks. In this talk, I will present next-generation autonomy stack architectures that treat learning and differentiability as first-class citizens, enabling training and evaluation with respect to downstream tasks without sacrificing interpretability, as well as methods for evaluating and generalizing them. Towards this end, I will present some of our recent research efforts, broadly spanning the topics of information representation and uncertainty propagation, simulation, and domain generalization." -youtube-code: "TBD" +youtube-code: "D_UQttGpN1E" --- diff --git a/_talks/parness_23.md b/_talks/parness_23.md index 676de95..db353d8 100644 --- a/_talks/parness_23.md +++ b/_talks/parness_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18" title: "Stowing and Picking Items in E-Commerce" abstract: "Stowing and picking items are two of the most expensive tasks in e-commerce fulfillment. They are difficult to automate because of 1) the many physical contacts between the robot and items already on shelves, 2) the variety of items that are handled, and 3) the financial motivation for storage density. This talk presents development of robotic manipulation capabilities for high clutter and high contact. Our perception algorithms infer available space using images of shelfs and manifest information. We then plan motions with an assumption of contact, and control those motions with force and torque in the loop. Custom end of arm tools (grippers) simplify the tasks." -youtube-code: "TBD" +youtube-code: "PrcypjtWse4" --- diff --git a/_talks/schwager_23.md b/_talks/schwager_23.md index 37bc7a9..8aa9e75 100644 --- a/_talks/schwager_23.md +++ b/_talks/schwager_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18" title: "Perception-Rich Robot Autonomy with Neural Environment Models" abstract: "New developments in computer vision and deep learning have led to the rise of neural environment representations: 3D maps that are stored as deep networks that spatially register occupancy, color, texture, and other physical properties. These environment models can generate photo-realistic synthetic images from unseen view points, and can store 3D information in exquisite detail. In this talk, I investigate the questions: How can robots use neural environment representations for perception, motion planning, manipulation, and simulation? I will present recent work from my lab in navigating a robot through a neural radiance field map of an environment while preserving safety guarantees. I will talk about realtime NeRF training, where we produce a neural map online in a SLAM-like fashion. I will also discuss open-vocabulary semantic navigation in a neural map, where we find or avoid objects specified at runtime. I will present the concept of dynamics-augmented neural objects, which are assets captured from RGB images whose motion (including contact) can be simulated in a differentiable physics engine. I will show how such models can be used in real-to-sim transfer and robot manipulation planning scenarios. I will conclude with future opportunities and challenges in integrating neural environment representations into the robot autonomy stack." -youtube-code: "TBD" +youtube-code: "eHr_jA8HnkA" --- diff --git a/_talks/student2fall_23.md b/_talks/student2fall_23.md index 47947df..8393ac3 100644 --- a/_talks/student2fall_23.md +++ b/_talks/student2fall_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18&srch=undefined" title: "Getting a (Gecko) Grip: Surface conformation for dry adhesion assisted robotic grasping" abstract: "Nature continues to stimulate engineering solutions for real world problems; understanding how the gecko, for example, relies on Van Der Waals forces to climb various surfaces inevitably led to the fabrication of materials which employ the same working principles. This talk addresses the question of how to develop gecko adhesive controllability with intermittently-active surface confirmation, for utilization in real world robotic applications. A study is performed on direct indenting as a manufacturing technique for creating varying micro-geometries for aiding gecko adhesive control and conformation. Building off this capability, augmented suction and adhesion tool for side-picking bulky and irregular objects is developed with air-promoted contact. There will be reference to manufacturing requirements of the dry adhesive material, and implementation considerations for tackling and improving robotic task execution. This work informs future design and use of gecko inspired adhesives with active surface conformation as a tool to effectively solve real world challenges." -youtube-code: "TBD" +youtube-code: "JL2UCAnyrjI" --- diff --git a/_talks/wissa_23.md b/_talks/wissa_23.md index f592b8a..47f67f8 100644 --- a/_talks/wissa_23.md +++ b/_talks/wissa_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18" title: "How Nature Moves: Exploring Locomotion in Various Mediums and Across Sizes" abstract: "Organisms have evolved various locomotion (self-propulsion) and shape adaptation (morphing) strategies to survive and thrive in diverse and uncertain environments. Unlike engineered systems, which rely heavily on active control, natural systems also rely on reflexive and passive control. Nature often exploits distributed flexibility to simplify global actuation requirements. These approaches to locomotion and morphing rely on multifunctional and passively adaptive structures. This talk will introduce several examples of bioinspired multifunctional structures, such as feather-inspired flow control devices. Flow control devices found on birds’ wings will be introduced as a pathway toward revolutionizing the current design and flight control of small-unmanned air vehicles. Wind tunnel and flight-testing results show the aerodynamic benefits of these devices in delaying stall and improving flight performance. In addition to bioinspired engineering, I will highlight how engineering analysis and experiments can help answer critical questions about biological systems, such as the flying fish aerial-aquatic transition and click beetles’ legless jumping. These research topics represent examples of how nature can inform robotic engineering design and highlight that engineering analysis can provide insights into the locomotion and adaptation strategies employed by nature." -youtube-code: "TBD" +# youtube-code: "TBD" --- diff --git a/_talks/zhao_23.md b/_talks/zhao_23.md index d239f39..71c83ff 100644 --- a/_talks/zhao_23.md +++ b/_talks/zhao_23.md @@ -7,5 +7,5 @@ location: Skilling Auditorium location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18" title: "Towards Trustworthy Autonomy - Generalizability, Safety, Embodiment" abstract: "As AI becomes more integrated into physical autonomy, it presents a dual spectrum of opportunities and risks. In this talk, I will introduce our efforts in creating trustworthy intelligent autonomy for vital civil usage such as self-driving cars and assistant robots. In these realms, training data often exhibit significant imbalance, multi-modal complexity, and inadequacy. I will initiate the discussion by analyzing 'long-tailed' problems with rare events and their connection to safety evaluation and safe reinforcement learning. I will then discuss how modeling multi-modal uncertainties as ‘tasks’ may enhance generalizability by learning across domains. To facilitate meta-learning and continuous learning with high-dimensional inputs in vision and language, we have developed prompt-transformer structures for efficient adaptation and mitigation of catastrophic forgetting. In cases involving unknown-unknown tasks with severely limited data, we explore the potential of leveraging external knowledge from legislative sources, causal reasoning, and large language models. Lastly, we will expand intelligence development into the realm of system-level design space with meta physical robot morphologies, which may achieve generalizability and safety more effectively than relying solely on software solutions." -youtube-code: "TBD" +youtube-code: "JSjWfGRGDHw" ---