When: 5.1.2022 at 15:00
Where: Zoom
Abstract: Most AI research focuses exclusively on the AI agent itself, i.e., given some input, what are the improvements to the agent’s reasoning that will yield the best possible output? In my research, I take a novel approach to increasing the capabilities of AI agents via the use of AI to design the environments in which they are intended to act. My methods identify the inherent capabilities and limitations of AI agents and find the best way to modify their environment in order to maximize performance. I will describe research projects that vary in their design objective, in the AI methodologies that are applied for finding optimal designs, and in the real-world applications to which they correspond. One example is Goal Recognition Design (GRD), which seeks to modify environments to allow an observing agent to infer the goals of acting agents as soon as possible. A second is Helpful Information Shaping (HIS), which seeks to find the minimal information to reveal to a partially-informed robot in order to guarantee the robot’s goal can be achieved. I will also show how HIS can be used in a market of information, where robots can trade their knowledge about the environment and achieve an effective communication that allows them to jointly maximize their performance. The third, Design for Collaboration (DFC), considers an environment with multiple self-interested reinforcement learning agents and seeks ways to encourage them to collaborate effectively. Throughout the talk, I will discuss how the different frameworks fit within my overarching objective of using AI to promote effective multi-agent collaboration and to enhance the way robots and machines interact with humans.
You can watch the seminar here
When: 12.1.2022 at 15:00
Where: zoom
Also: Dan Kahn Building, room 217, Technion
Abstract: It seems that animals are very good at learning how to move, and how to recover the ability to move after injury. Roboticists have attempted to imbue the same capabilities in robots with only moderate success. Through pursuing a deeper understanding of the underlying mathematics and physics of locomotion, I present the idea of using limit cycle oscillators as the key mathematical object to consider. Using tools developed for modeling the oscillators that appear in biological locomotion and combining them with insights from geometric mechanics, we created robots that can learn how to move with an optimization that lasts only a few dozens of cycles. The talk will present these ideas at a high level, primarily focusing on experimental results from animals, robots, and simulated robots.
You can watch the seminar here
Work towards MSc degree under the supervision of Prof. Vadim Indelman
When: 9.2.2022 at 11:00
Where: zoom
Abstract: Robust perception is a key required capability in robotics and AI when dealing with scenarios and environments that exhibit some level of ambiguity and perceptual aliasing. In this work, we consider such a setting and contribute a framework that enables to update probabilities of externally-defined data association hypotheses from some past time with new information that has been accumulated until the current time. In particular, we show appropriately updating probabilities of past hypotheses within this smoothing perspective potentially enables to disambiguate these hypotheses even when there is no full disambiguation of the mixture distribution at the current time. Further, we develop an incremental algorithm that re-uses hypotheses’ weight calculations from previous steps, thereby reducing computational complexity. In addition, we show how our approach can be used to enhance current-time hypotheses pruning, by discarding corresponding branches in the hypotheses tree. We demonstrate our approach in simulation, considering an extremely aliased environment setting.
You can see the seminar here
When: 22.12.2021 at 15:00
Where: Zoom
Abstract: Spatial perception —the robot’s ability to sense and understand the surrounding environment— is a key enabler for autonomous systems operating in complex environments, including self-driving cars and unmanned aerial vehicles. Recent advances in perception algorithms and systems have enabled robots to detect objects and create large-scale maps of an unknown environment, which are crucial capabilities for navigation, manipulation, and human-robot interaction. Despite these advances, researchers and practitioners are well aware of the brittleness of existing perception systems, and a large gap still separates robot and human perception. This talk discusses two efforts targeted at bridging this gap. The first effort targets high-level understanding. While humans are able to quickly grasp both geometric, semantic, and physical aspects of a scene, high-level scene understanding remains a challenge for robotics. I present our work on real-time metric-semantic understanding and 3D Dynamic Scene Graphs. I introduce the first generation of Spatial Perception Engines, that extend the traditional notions of mapping and SLAM, and allow a robot to build a “mental model” of the environment, including spatial concepts (e.g., humans, objects, rooms, buildings) and their relations at multiple levels of abstraction. The second effort focuses on robustness. I present recent advances in the design of certifiable perception algorithms that are robust to extreme amounts of noise and outliers and afford performance guarantees. I present fast certifiable algorithms for object pose estimation and showcase an application to vehicle pose and shape estimation in self-driving scenarios.
You can watch the seminar here
When: 8.12.2021 at 15:00
Where: Zoom
Abstract: Minimally invasive surgery has become the best practice for many surgical procedures due to its fast recovery and minimal damage to the patient, robotic surgery is also becoming more popular and offers the surgeon better ergonomics, stability of the line of sight, and fine movements. In this lecture, we will review recent technologies employed in robotic surgery for reducing the surgeon’s intensive labor and workload, image processing capabilities allow for autonomous movements of the camera during surgery, and 3D reconstruction for fast physical measurements. We will see how machine learning and scene understanding can further improve the surgical procedure.
When: 10.11.2021 at 15:00
Where: Zoom
Abstract: In the talk, the methods of curvature-constrained trajectory planning will be discussed in the context of multi-goal planning. The studied problems are variants of the well-known combinatorial optimization, the Traveling Salesman Problem, extended for the travel cost of curvature-constrained trajectories. The lecture will introduce the role of the Dubins vehicle model in the solution quality assessment of the curvature-constrained multi-goal planning. Furthermore, recent advancements on the solution of Dubins routing problems will be presented.
You can watch the seminar here
When: 3.11.2021 at 15:30
Where: Zoom
Abstract: Factor graphs have become a popular tool for modeling robot perception problems. Not only can they model the bipartite relationship between sensor measurements and variables of interest for inference, but they have also been instrumental in devising novel inference algorithms that exploit the spatial and temporal structure inherent in these problems. I will start with a brief history of these inference algorithms and relevant applications. I will then discuss open challenges in particular related to robustness from the inference perspective and discuss some recent steps towards more robust perception algorithms.
You can watch the seminar here
Work towards PhD degree under the supervision of Prof. Erez Karps and Prof. Emeritus Per-Olof Gutman
When: 4.11.2021 at 11:00
Where: Zoom
Abstract: Robots operate in the real world, which is hybrid, i.e. comprised of continuous and discrete properties, uncertain, constrained, non-linear, and often cooperation or at least synchronization with other agents, human and robotic is required. Moreover, usually for an agent to reach its desired goal, a long time horizon is required, which accumulates errors and makes it dimensionally impossible to discretize the problem. Each of these separately poses a major challenge for autonomous behavior. Robots must be able to come up with long-term plans in the face of these challenges in order to reach autonomy. Various communities have addressed these problems; e.g., the control, automated planning, machine learning, and robotics communities, each with its own merits and weaknesses. In this work, we attempt to bridge the gap between these communities and present a unified method leveraging accurate short-term control strategy, long-term abstract planning methods, and deep neural networks tailored on the fly. Our method can handle long, continuous horizons, allowing for concurrency and synchronization, incorporation of accurate non-linear dynamic models, while balancing between expensive accurate computations and “simple” abstract computations.
You can watch the seminar here