News & Events


TRS - Prof. Luca Carlone (MIT) - From SLAM to Real-time Scene Understanding: 3D Dynamic Scene Graphs and Certifiable Perception Algorithms

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




TRS - Dr. Tal Nir (principal computer vision engineer in Asensus surgical) - Robotic minimally invasive surgery - current and future trends

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.


TRS - Prof. Jan Faigl (Czech Technical University in Prague, Artificial Intelligence Center) - Curvature-constrained multi-goal trajectory planning

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



TRS - Prof. Michael Kaess (Robotics Institute, Carnegie Mellon University) - Factor Graphs and Robust Perception

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


TASP PhD Seminar - Ayal Taitler - "A Method for Combined Planning and Control for Hybrid Domains"

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


TASP MSc Seminar - Sapir Gershov -Autonomous Medical Simulators

Work towards MSc degree under the supervision of Dr. Shlomi Laufer

When: 29.9.2021 at 14:00

Where: zoom

Abstract:   Medical simulators provide a controlled environment for training and assessing clinical skills. However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist. This makes the assessment process inefficient and expensive. Furthermore, this evaluation method does not provide medical practitioners the opportunity for independent training. Ideally, the process of managing the simulation should be done by a fully aware objective system, capable of recognizing and monitoring the clinical performance and to act accordingly. In our study we applied techniques from graph networks and language models to construct a fully autonomic simulation framework, based on clinical data collected from 28 medical simulations. A key finding of our work is that by analyzing physicians’ speech alone, we can successfully perform state estimation and to make predictions regarding their medical treatment planning. We propose that the fully autonomic speech-based framework for managing medical simulations constructed in this study is applicable to clinical practice. In a field where seconds can make the difference between life and death, integrating an autonomic assisting tool is of great importance.


TASP MSc Seminar - Rotem Levy -Path and Trajectory Planning for Autonomous Vehicles on Roads without Lanes

Work towards MSc degree under the supervision of Assoc. Prof. Jack Hadad

When: 2.9.2021 at 15:00

Where: Zoom

Abstract: Autonomous vehicles traveling without considering the lane marks and utilizing all road width have an opportunity to maximize the vehicles’ performance. By taking advantage of the entire width of curvy roads and the cooperative behavior of connected autonomous vehicles, new options for path planning can be implemented while utilizing the existing infrastructure. This research focuses on path and trajectory planning for fully autonomous vehicles without considering the lane marks by a proposed controller. This cooperative controller uses the nonlinear model predictive control (NMPC) approach for dozens of autonomous vehicles in the existing road infrastructure. The controller maximizes vehicles’ progress on the road with minimal control efforts while complying with the design constraints imposed by the road geometry, distances between vehicles, and vehicle dynamics. As a result, the controller generates the longitudinal acceleration and the steering rate inputs. The controller was tested on several case study simulations. The tests were done on closed-loop tracks and straight roads with different numbers of vehicles with identical vehicles’ parameters and different vehicles’ parameters to examine the advantages of the lane-free road concept. As part of the simulations, a comparison of the lane-free concept and the “traditional” lane concept showed that the lane-free concept proved to be better in the examined case studies. In addition, lab experiments were conducted on three robots performing several case studies.

You can watch the seminar here


TASP PhD Seminar - Ilana Segall - "Broadcast Guidance of Multi-Agent Systems "

Work towards PhD degree under the supervision of  Prof. Alfred M. Bruckstein

When: 12.9.2021 at 11:00 

Where: Zoom

Abstract: The thesis investigates the emergent behavior of  single integrator multi-agents, guided by an exogenous broadcast control. The broadcast guidance control, a velocity signal, is detected and applied by a subgroup of agents, referred to as leaders.  Several linear and non-linear models are considered, with fixed, as well as varying topology, the latter caused by limited visibility. In each case, we show the impact of the broadcast control and of the subset of leaders on the asymptotic behavior of the system.