Embedded Files
April 2026
April 2026
April 30: AERObotics: The Art of Catching Objects in the Air
April 30: AERObotics: The Art of Catching Objects in the Air
Speaker: Prof. Debasish Ghose (Indian Institute of Science, Bangalore )
Speaker: Prof. Debasish Ghose (Indian Institute of Science, Bangalore )
Abstract: A branch of robotics, called mobile robotics, looks at problems related to guiding robots to move over a terrain. Robots in the air are aerial robots and often have similar requirements of guiding themselves from one point to another in the air. A particularly interesting problem is when the robot has to intercept a moving object flying in the air. For instance, how can a robot gripper mounted on a drone catch a ball in the air? In this talk we will see how ideas from the literature on intercepting targets work in the domain of aerial robots.
Abstract: A branch of robotics, called mobile robotics, looks at problems related to guiding robots to move over a terrain. Robots in the air are aerial robots and often have similar requirements of guiding themselves from one point to another in the air. A particularly interesting problem is when the robot has to intercept a moving object flying in the air. For instance, how can a robot gripper mounted on a drone catch a ball in the air? In this talk we will see how ideas from the literature on intercepting targets work in the domain of aerial robots.
Speaker Bio: Prof. Debasish Ghose is an honorary professor at the Indian Institute of Science, Bengaluru. Earlier he was a professor at the same institute in both the Department of Aerospace Engineering and the Robert Bosch Centre for Cyber-physical Systems. He works in the area of guidance and control of autonomous systems, robotics, and game theory. He has held various visiting positions such as at the University of California at Los Angeles, the University of Leicester, and the University of Exeter in the UK. He is a fellow of INAE, NASI, INSA, AAAI, and an associate fellow of AIAA.
Youtube Recording: @Robotics Café
May 2026
May 2026
May 7: Leveraging Predictive Uncertainty for Reliable Model-Based Planning and Control
May 7: Leveraging Predictive Uncertainty for Reliable Model-Based Planning and Control
Speaker: Prof. Arun Kumar Singh (University of Tartu, Estonia)
Speaker: Prof. Arun Kumar Singh (University of Tartu, Estonia)
Abstract: Model-based planning and control remains a dominant paradigm for autonomous decision-making in robotics. However, the underlying models—whether representing robot dynamics or complex environmental interactions—are inherently prone to error. In this talk, I will emphasize the critical need to quantify the uncertainty associated with these learned models and demonstrate how such estimates can be everaged to achieve robust planning and control. I will highlight recent advancements, including my group's latest work on learning uncertainty in a task-aware manner. To ground these theoretical insights, I will showcase practical applications in monocular image-based navigation and autonomous driving.
Abstract: Model-based planning and control remains a dominant paradigm for autonomous decision-making in robotics. However, the underlying models—whether representing robot dynamics or complex environmental interactions—are inherently prone to error. In this talk, I will emphasize the critical need to quantify the uncertainty associated with these learned models and demonstrate how such estimates can be everaged to achieve robust planning and control. I will highlight recent advancements, including my group's latest work on learning uncertainty in a task-aware manner. To ground these theoretical insights, I will showcase practical applications in monocular image-based navigation and autonomous driving.
Speaker Bio: Prof. Arun Kumar Singh is a Professor of Motion Planning at Institute of Technology, University of Tartu, Estonia. He is the PI of the AKS Lab. His researchc interests lie in developing algorithmic foundations of robotics, especially motion planning, control and learning4control. His group consistently publishes in top robotics conferences and journals. He is currently leading a 8M Eruopean project XSCAVE which aims to develop AI based planners and controllers for safety-critical operations involving heavy machineries in Forestry, Logistics and Construction.
Youtube Recording: @ Robotics Café
May 14: Passive dynamics is a good basis for robot design and control. Not.
May 14: Passive dynamics is a good basis for robot design and control. Not.
Speaker: Prof. Andy Ruina (Cornell University, USA)
Speaker: Prof. Andy Ruina (Cornell University, USA)
Abstract: Many airplanes can, or nearly can, glide stably without control. So, it seems natural that the first successful powered flight followed from mastery of gliding. Many bicycles can, or nearly can, when in motion, balance themselves. Bicycle design seems to have evolved to have this feature. Also, we can make toys and ‘robots’ that, like a stable glider or coasting bicycle, stably walk without motors or control in a remarkably human-like way. Again, it thus seems to make sense to use `passive-dynamics’ (uncontrolled dynamics) as a core for developing the control of walking robots and to gain understanding of the control of walking people.
Abstract: Many airplanes can, or nearly can, glide stably without control. So, it seems natural that the first successful powered flight followed from mastery of gliding. Many bicycles can, or nearly can, when in motion, balance themselves. Bicycle design seems to have evolved to have this feature. Also, we can make toys and ‘robots’ that, like a stable glider or coasting bicycle, stably walk without motors or control in a remarkably human-like way. Again, it thus seems to make sense to use `passive-dynamics’ (uncontrolled dynamics) as a core for developing the control of walking robots and to gain understanding of the control of walking people.
That's what I used to think. But, so far, this passive approach has not led to robust walking robots. And, when looked at more carefully, the airplane and bicycle motivations are not as suggestive as they seemed. What about human evolution? Unlike what would be indicated by the passive-dynamic paradigm, human beings did not evolve dynamic bodies and then learn to control them. Rather, people had elaborate control systems way back when we were fish and even when we were worms with no skeletons.
That's what I used to think. But, so far, this passive approach has not led to robust walking robots. And, when looked at more carefully, the airplane and bicycle motivations are not as suggestive as they seemed. What about human evolution? Unlike what would be indicated by the passive-dynamic paradigm, human beings did not evolve dynamic bodies and then learn to control them. Rather, people had elaborate control systems way back when we were fish and even when we were worms with no skeletons.
Now, instead of thinking of good powered walking as passive walking with a small amount of control added, I think of good powered walking, human or robotic, as highly controlled, while optimized mostly for avoiding falls and, secondarily, for minimal actuator use. However, if control is paramount, why is it that uncontrolled passive-dynamic walkers walk so much like humans? It seems that energy optimal, yet robust, control, which is perhaps a proxy for the evolutionary development of human coordination, arrives at solutions that have some features in common with passive-dynamics. Such optimized control, when well done, has most of the motor effort titrated out, yet always at the ready. Thus, highly controlled systems can deceptively-look almost “passive”.
Now, instead of thinking of good powered walking as passive walking with a small amount of control added, I think of good powered walking, human or robotic, as highly controlled, while optimized mostly for avoiding falls and, secondarily, for minimal actuator use. However, if control is paramount, why is it that uncontrolled passive-dynamic walkers walk so much like humans? It seems that energy optimal, yet robust, control, which is perhaps a proxy for the evolutionary development of human coordination, arrives at solutions that have some features in common with passive-dynamics. Such optimized control, when well done, has most of the motor effort titrated out, yet always at the ready. Thus, highly controlled systems can deceptively-look almost “passive”.
The challenge in robotics control today is the posing and solving of appropriate optimization problems. One needs an appropriate control architecture and an appropriate objective (“loss function”). The optimization problem needs to be solvable, or approximately solvable, and the model-based solution policy needs to be robust against unknown disturbances and unknown modeling errors.
The challenge in robotics control today is the posing and solving of appropriate optimization problems. One needs an appropriate control architecture and an appropriate objective (“loss function”). The optimization problem needs to be solvable, or approximately solvable, and the model-based solution policy needs to be robust against unknown disturbances and unknown modeling errors.
Speaker Bio: Prof. Andy Ruina's graduate education was in solid mechanics (in particular, friction) at Brown University. Those interests morphed into biomechanics, dynamics and robotics. Recently, he is primarily interested in the mechanics and control of underactuated motion and locomotion. His lab has made some of the most energy-effective walking robots. See ruina.org for more information.
Youtube Recording: Unfortunately, the recording for this session could not be preserved due to a technical issue.
May 21: UAV Development Cycle: Technical Benchmarks and Lessons in Innovation
May 21: UAV Development Cycle: Technical Benchmarks and Lessons in Innovation
Speaker: Dr. Tanvi Prakash (L&T Precision Engineering and Systems, India)
Speaker: Dr. Tanvi Prakash (L&T Precision Engineering and Systems, India)
Abstract: This session aims to provide participants with an understanding of the latest practices in design and development of high-performance UAVs, drawing on perspectives and challenges from ongoing programmes in the Indian industry. The session will walk participants through the stages of the UAV development cycle, starting from conceptual design and initial sizing, to detailed design and prototyping, and flight-test engineering. The talk aims to give an insight into the technical and logistical rigour involved, with a real- world focus, including some interesting case studies from L&T's UAV portfolio. The talk will show proven ways in which designs can be taken from paper to actual field conditions, by surmounting unique challenges such as terrain, weather, and operational aspects to develop a product that satisfactorily meets customer expectations. The session is targeted at professionals and students, interested in Aircraft Design, New Product Development, Prototyping, and Flight Test Engineering.
Abstract: This session aims to provide participants with an understanding of the latest practices in design and development of high-performance UAVs, drawing on perspectives and challenges from ongoing programmes in the Indian industry. The session will walk participants through the stages of the UAV development cycle, starting from conceptual design and initial sizing, to detailed design and prototyping, and flight-test engineering. The talk aims to give an insight into the technical and logistical rigour involved, with a real- world focus, including some interesting case studies from L&T's UAV portfolio. The talk will show proven ways in which designs can be taken from paper to actual field conditions, by surmounting unique challenges such as terrain, weather, and operational aspects to develop a product that satisfactorily meets customer expectations. The session is targeted at professionals and students, interested in Aircraft Design, New Product Development, Prototyping, and Flight Test Engineering.
Speaker Bio: Dr. Tanvi Prakash is an Aircraft Designer serving as Deputy General Manager (UAV) at Larsen and Toubro, a leading Indian defence contractor. She is an alumnus of VJTI Mechanical (2011) and IIT Bombay Aerospace. She has received the prestigious INAE Young Associate award and the Women in Aviation - Harvard scholarship for Emerging Leaders in Aviation. Her areas of expertise are Aircraft Design, New Product Development, and Flight Test Engineering. Her most recent UAV development assignment was leading the design and development of Teer, a jet-powered aerial target UAV, for the Indian Army. Dr. Tanvi led L&T’s team across the entire development cycle, including Design, Prototyping, Flight Testing, and Serial Production. She also serves as a UAV test pilot, conducting trials across India for various defence customers across India. Currently, she focuses on advancing innovative UAV configurations like tiltrotors, flying bullets, and morphing wings.
Youtube Recording: @ Robotics Café
May 28 (Talk-1): Not Just Where to Move, But How to Coordinate: The Challenge of Dual-Arm Manipulation
May 28 (Talk-1): Not Just Where to Move, But How to Coordinate: The Challenge of Dual-Arm Manipulation
Speaker: Debojit Das (M. Tech. Student, Indian Institute of Technology Gandhinagar)
Speaker: Debojit Das (M. Tech. Student, Indian Institute of Technology Gandhinagar)
Abstract: A robot with two arms does not automatically become twice as capable; sometimes, it becomes twice as confused. Many failures in manipulation do not happen because the robot lacks a trajectory or goal, but because it does not know how the relationship between its arms should evolve during the task, especially in the face of perturbations, or asymmetry in conditions. Through examples such as handovers, pouring, insertion, and cooperative transport, this talk looks at the hidden coordination problem behind dual-arm manipulation, and discusses how structured coordination primitives may offer a path toward more general and reliable robot behavior. The talk addresses two central questions, how a robot can use language and visual context to decide what kind of coordination is needed in the first place, and how it can execute such coordinated control reliably.
Abstract: A robot with two arms does not automatically become twice as capable; sometimes, it becomes twice as confused. Many failures in manipulation do not happen because the robot lacks a trajectory or goal, but because it does not know how the relationship between its arms should evolve during the task, especially in the face of perturbations, or asymmetry in conditions. Through examples such as handovers, pouring, insertion, and cooperative transport, this talk looks at the hidden coordination problem behind dual-arm manipulation, and discusses how structured coordination primitives may offer a path toward more general and reliable robot behavior. The talk addresses two central questions, how a robot can use language and visual context to decide what kind of coordination is needed in the first place, and how it can execute such coordinated control reliably.
Speaker Bio: Debojit Das is an early-career robotics researcher working at the intersection of bimanual manipulation, robotic grasping, control, and coordination. He is currently pursuing a B.Tech-M.Tech Dual Degree in Mechanical Engineering at the Indian Institute of Technology Gandhinagar, where he is a member of the IITGN Robotics Lab and is advised by Prof. Harish P. M. He is presently a visiting researcher at the Smart Robots Design Lab, Tohoku University, Japan, where his work focuses on developing robotic systems capable of more intelligent and coordinated real-world bimanual manipulation.
He has previously worked with the Advanced Robotics Team at Addverb Technologies, contributing to robotics research and development from an industrial perspective. Beyond research, he has also been a TEDx speaker and has spoken at ROSCon India 2025, where he shared his work and ideas with broader technical and public audiences.
More information about his work can be found at Website and Github.
Youtube Recording: @ Robotics Café
May 28 (Talk-2): Geometry-Grounded Segment Matching for Robust Robot Perception
May 28 (Talk-2): Geometry-Grounded Segment Matching for Robust Robot Perception
Speaker: Rohit Jayanti (Doctoral Student, IIIT-Hyderabad)
Speaker: Rohit Jayanti (Doctoral Student, IIIT-Hyderabad)
Abstract: Modern vision models allow robots to perceive the physical world through semantically meaningful image segments rather than fragile pixel-level keypoints. However, associating these segments across wide-baseline viewpoints, where perspective shifts and up to 180-degree rotations occur, remains a major challenge for standard 2D appearance-based encoders. This talk will discuss SegMASt3R (presented as a Spotlight at NeurIPS 2025), a framework that leverages the spatial inductive biases of 3D foundation models to achieve robust wide-baseline segment matching. This approach outperforms state-of-the-art baselines, including SAM2 and DINOv2, by up to 30% in matching accuracy in challenging settings. Furthermore, the talk will discuss how accurate segment matching translates directly to significant performance gains in key downstream robotics tasks, namely 3D Instance Mapping and Object-Relative Topological Navigation.
Abstract: Modern vision models allow robots to perceive the physical world through semantically meaningful image segments rather than fragile pixel-level keypoints. However, associating these segments across wide-baseline viewpoints, where perspective shifts and up to 180-degree rotations occur, remains a major challenge for standard 2D appearance-based encoders. This talk will discuss SegMASt3R (presented as a Spotlight at NeurIPS 2025), a framework that leverages the spatial inductive biases of 3D foundation models to achieve robust wide-baseline segment matching. This approach outperforms state-of-the-art baselines, including SAM2 and DINOv2, by up to 30% in matching accuracy in challenging settings. Furthermore, the talk will discuss how accurate segment matching translates directly to significant performance gains in key downstream robotics tasks, namely 3D Instance Mapping and Object-Relative Topological Navigation.
Speaker Bio: Rohit is a Graduate Researcher at the Robotics Research Center (RRC), IIIT-Hyderabad, advised by Prof. Madhav Krishna, where his work focuses on 3D scene understanding, geometric foundation models, and topological robot navigation. Previously, he was a founding engineer at Spatial Guide, an augmented-reality (AR) startup accelerated through the Techstars Workforce Development program (in Denver, Colorado, Cohort of 2022).
Youtube Recording: @ Robotics Café
June 2026
June 2026
June 4: Robotics at Crossroads: Is E2E Systems the Way Forward?
June 4: Robotics at Crossroads: Is E2E Systems the Way Forward?
Speaker: Prof. K Madhava Krishna (IIIT Hyderabad, India)
Speaker: Prof. K Madhava Krishna (IIIT Hyderabad, India)
Abstract: Traditional robotic systems have relied extensively on modularity, where perception and planning modules work in tango, wherein the perception stack brings together SLAM, relocalization and the navigation block comprises of planning, navigation, collision avoidance and control. While modularity brings about explainability and interpretability there are the challenges associated with propagation and amplification of errors via the modules and the challenges of modules being developed in silos where the left hand is unaware of what the right hand does. In this talk we look at how Post Modern AI and its advocacy of complete end2end systems is impacting robotics and argue how a judicious mix of classical robotics stack with the modern AI stack could still be the way to go especially where compute and data are at a premium and deployability is paramount. This argument stems though case studies we present based on our diverse experiences at the Robotics Research Center, IIIT Hyderabad. These test cases include both indoor and outdoor navigation as well as robotic manipulation.
Abstract: Traditional robotic systems have relied extensively on modularity, where perception and planning modules work in tango, wherein the perception stack brings together SLAM, relocalization and the navigation block comprises of planning, navigation, collision avoidance and control. While modularity brings about explainability and interpretability there are the challenges associated with propagation and amplification of errors via the modules and the challenges of modules being developed in silos where the left hand is unaware of what the right hand does. In this talk we look at how Post Modern AI and its advocacy of complete end2end systems is impacting robotics and argue how a judicious mix of classical robotics stack with the modern AI stack could still be the way to go especially where compute and data are at a premium and deployability is paramount. This argument stems though case studies we present based on our diverse experiences at the Robotics Research Center, IIIT Hyderabad. These test cases include both indoor and outdoor navigation as well as robotic manipulation.
Speaker Bio: Prof. K Madhava Krishna, FNAE, is the Raj Reddy Endowment Chair Professor and heads the Robotics Research Center (RRC), at IIIT Hyderabad for the last 21 years and the Kohli Center for Intelligent Systems, (KCIS) for the last 5 years. His primary research areas include Robotic Vision, Autonomous Navigation as Applied to Drones, Indoor and Outdoor Robots . RRC is nationally renowned and internationally acknowledged for its contributions to robotics research and is ranked number one in the country, top 10 in Asia and amongst top 50 in the world according to csrankings.org , where the rankings are based on publications in premier robotics forums. RRC works closely with the industry, public organizations, defense labs and the GoI on many funded projects and research collaborations on diverse set of areas ranging from robotic vision and learning to dynamics and control.
Youtube Recording: @ Robotics Café
June 11: Fast Feature Field (F³): A Predictive Representation of Events
June 11: Fast Feature Field (F³): A Predictive Representation of Events
Speaker: Prof. Pratik Chaudhari (University of Pennsylvania, USA)
Speaker: Prof. Pratik Chaudhari (University of Pennsylvania, USA)
Abstract: The human eye perceives its environment quite differently from a phone camera. While the camera creates an array of pixels---roughly 50 million of them in modern phones---the eye is more judicious. It triggers a message to the brain only when the intensity of light at a pixel changes beyond a threshold. This is why our eyes can register very fast motion and see much better in dim light or across illumination changes. “Event cameras” emulate this processing in the human eye. But since they do not really create an “image”, working with these new cameras requires rethinking a lot of fundamentals of computer vision. I will first describe a mathematical argument for building representations of event camera that retain information about structure and motion in the scene. This representation, that we call fast feature field (F³) is obtained by learning to predict future events from past events. F³ is effectively a multi-channel continuous-time video, and it can be computed at more than 120 Hz on HD data on a desktop GPU. It can be used to perform tasks such as optical flow estimation, semantic segmentation, and monocular metric depth estimation at 25—75 Hz. F³ works robustly on data from different robotic platforms, across different lighting conditions and environments. One of the biggest challenges of working with event cameras is the lack of co-registered, time-synchronized robot perception data that can be used for cross-annotations. To that end, I will also describe ongoing work on Neurosim, a high-performance library for simulating dynamic vision sensors, RGB cameras, depth sensors and inertial sensors, and agile quadrotor dynamics. It can achieve frame rates as high as 2700 FPS on a desktop GPU and 1.5 kHz closed-loop real-time control. Neurosim and its accompanying communication library called Cortex unlock downstream applications such as online self-supervised learning of neuromorphic perception and control.
Abstract: The human eye perceives its environment quite differently from a phone camera. While the camera creates an array of pixels---roughly 50 million of them in modern phones---the eye is more judicious. It triggers a message to the brain only when the intensity of light at a pixel changes beyond a threshold. This is why our eyes can register very fast motion and see much better in dim light or across illumination changes. “Event cameras” emulate this processing in the human eye. But since they do not really create an “image”, working with these new cameras requires rethinking a lot of fundamentals of computer vision. I will first describe a mathematical argument for building representations of event camera that retain information about structure and motion in the scene. This representation, that we call fast feature field (F³) is obtained by learning to predict future events from past events. F³ is effectively a multi-channel continuous-time video, and it can be computed at more than 120 Hz on HD data on a desktop GPU. It can be used to perform tasks such as optical flow estimation, semantic segmentation, and monocular metric depth estimation at 25—75 Hz. F³ works robustly on data from different robotic platforms, across different lighting conditions and environments. One of the biggest challenges of working with event cameras is the lack of co-registered, time-synchronized robot perception data that can be used for cross-annotations. To that end, I will also describe ongoing work on Neurosim, a high-performance library for simulating dynamic vision sensors, RGB cameras, depth sensors and inertial sensors, and agile quadrotor dynamics. It can achieve frame rates as high as 2700 FPS on a desktop GPU and 1.5 kHz closed-loop real-time control. Neurosim and its accompanying communication library called Cortex unlock downstream applications such as online self-supervised learning of neuromorphic perception and control.
Speaker Bio: Prof. Pratik Chaudhari is an Assistant Professor in Electrical and Systems Engineering and Computer and Information Science at the University of Pennsylvania. He is a core member of the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory. From 2018-19, he was a Senior Applied Scientist at Amazon Web Services and a Postdoctoral Scholar in Computing and Mathematical Sciences at Caltech. Pratik received his PhD (2018) in Computer Science from UCLA, and his Master's (2012) and Engineer's (2014) degrees in Aeronautics and Astronautics from MIT. He was a part of NuTonomy Inc. (now Hyundai-Aptiv Motional) from 2014-16. He is the recipient of the Amazon Machine Learning Research Award (2020), NSF CAREER award (2022) and the Intel Rising Star Faculty Award (2022).
Youtube Recording: @ Robotics Café
June 18: From Simulation to Sky: Developing Autonomous UAVs with MATLAB
June 18: From Simulation to Sky: Developing Autonomous UAVs with MATLAB
Speaker: Dr. N Dhananjay (MathWorks India Pvt. Ltd., Bengaluru, India)
Speaker: Dr. N Dhananjay (MathWorks India Pvt. Ltd., Bengaluru, India)
Abstract: Developing autonomous UAVs requires navigating a broad engineering stack — vehicle dynamics, sensor modeling, path planning, mission management, and real-time communication — while managing the risk and cost of hardware testing. This talk presents the MATLAB UAV Toolbox as an end-to-end framework for taking a UAV algorithm from first-principles simulation to real flight, covering both fixed-wing and multirotor vehicle types. We walk you through the toolbox's layered architecture: reduced-order guidance models (6-state fixed-wing, 12-state multirotor) that support automatic code generation; a scenario simulation environment with LiDAR, GPS/INS, radar, and atmospheric turbulence models; and a path planning suite spanning 3D Dubins curves, waypoint following, coverage planning, and Vector Field Histogram obstacle avoidance. We then show how Simulink can be used to enable model-based design workflows. We highlight software-in-the-loop (SITL), hardware-in-the-loop (HITL), and deployment to PX4 and ArduPilot autopilots via auto-generated C code. We discuss the MAVLink communication layer, including a microservice pattern that abstracts parameter management, command dispatch, and mission upload behind high-level MATLAB classes. We present post-flight analysis workflows — parsing ArduPilot and PX4 logs into MATLAB timetables, system identification techniques for refining aerodynamic and wind turbulence models from flight data. The talk showcases important UAV workflows of interest for both academia and industry.
Abstract: Developing autonomous UAVs requires navigating a broad engineering stack — vehicle dynamics, sensor modeling, path planning, mission management, and real-time communication — while managing the risk and cost of hardware testing. This talk presents the MATLAB UAV Toolbox as an end-to-end framework for taking a UAV algorithm from first-principles simulation to real flight, covering both fixed-wing and multirotor vehicle types. We walk you through the toolbox's layered architecture: reduced-order guidance models (6-state fixed-wing, 12-state multirotor) that support automatic code generation; a scenario simulation environment with LiDAR, GPS/INS, radar, and atmospheric turbulence models; and a path planning suite spanning 3D Dubins curves, waypoint following, coverage planning, and Vector Field Histogram obstacle avoidance. We then show how Simulink can be used to enable model-based design workflows. We highlight software-in-the-loop (SITL), hardware-in-the-loop (HITL), and deployment to PX4 and ArduPilot autopilots via auto-generated C code. We discuss the MAVLink communication layer, including a microservice pattern that abstracts parameter management, command dispatch, and mission upload behind high-level MATLAB classes. We present post-flight analysis workflows — parsing ArduPilot and PX4 logs into MATLAB timetables, system identification techniques for refining aerodynamic and wind turbulence models from flight data. The talk showcases important UAV workflows of interest for both academia and industry.
Speaker Bio: Dr. N Dhananjay is a Senior Software Engineer at MathWorks, India. He is a member of the Autonomous Aerial Systems Group. Prior to joining MathWorks in 2017, he worked as an Image Processing Engineer at UURMI System Pvt. Ltd., Hyderabad, India where he worked on the Autonomous Landing of UAV project. Dhananjay received his PhD in Aerospace Engineering from IISc, Bangalore, and his Master's in Electrical Engineering also from IISc. Currently he is working on developing tools in MATLAB/SIMULINK for autonomous UAV applications.
Youtube Recording: TBA.
June 25 (Talk 1): DOME: Distributed Online Learning-based Multi-Estimate Fusion for Cooperative Predictive Target Tracking Using a Robotic Swarm
June 25 (Talk 1): DOME: Distributed Online Learning-based Multi-Estimate Fusion for Cooperative Predictive Target Tracking Using a Robotic Swarm
Speaker: Shubhankar Gupta (IISc Bengaluru, India)
Speaker: Shubhankar Gupta (IISc Bengaluru, India)
Abstract: Cooperative predictive target tracking is a critical capability for robotic swarms, but real-world deployments are often plagued by high prediction bias and communication uncertainty. In this talk, we investigate the challenge of target tracking when robots interact over a randomly time-varying communication network and exhibit significant heterogeneity in their onboard sensors and prediction algorithms. To address these challenges, I will introduce the Distributed Online learning-based Multi-Estimate (DOME) fusion algorithm. DOME performs a collaborative, weighted fusion of local and socially shared predictions, adapting fusion weights online using direct feedback from prediction loss rather than relying on traditional covariance information. I will present the theoretical foundations of the algorithm and establish the convergence of the conditional expectations of the fusion weights under reasonable assumptions. Finally, I will demonstrate DOME's efficacy through comprehensive performance and scalability tests, showing that DOME achieves up to 74% lower prediction loss compared to covariance-based and online learning-based decentralized fusion baselines, particularly in environments with significant model drift and unreliable communication.
Abstract: Cooperative predictive target tracking is a critical capability for robotic swarms, but real-world deployments are often plagued by high prediction bias and communication uncertainty. In this talk, we investigate the challenge of target tracking when robots interact over a randomly time-varying communication network and exhibit significant heterogeneity in their onboard sensors and prediction algorithms. To address these challenges, I will introduce the Distributed Online learning-based Multi-Estimate (DOME) fusion algorithm. DOME performs a collaborative, weighted fusion of local and socially shared predictions, adapting fusion weights online using direct feedback from prediction loss rather than relying on traditional covariance information. I will present the theoretical foundations of the algorithm and establish the convergence of the conditional expectations of the fusion weights under reasonable assumptions. Finally, I will demonstrate DOME's efficacy through comprehensive performance and scalability tests, showing that DOME achieves up to 74% lower prediction loss compared to covariance-based and online learning-based decentralized fusion baselines, particularly in environments with significant model drift and unreliable communication.
Speaker Bio: Shubhankar Gupta is a Ph.D. candidate in the Department of Aerospace Engineering at the Indian Institute of Science (IISc), Bengaluru, India. His research in the AI and Robotics Lab (AIRL) focuses on distributed online learning, swarm robotics, multi-agent systems, decentralized optimization, and control theory. Supported by the 2024 KIAC Ph.D. Fellowship, his work has appeared in venues like the Transactions on Machine Learning Research (TMLR) and the AAAI Conference on Artificial Intelligence (AAAI). He received an M.S. in Aeronautics and Astronautics from Purdue University (West Lafayette, IN, US) and a B.Tech. in Mechanical Engineering from the Indian Institute of Technology Kanpur (IITK), and previously worked as a Project Assistant at the National Aerospace Laboratories (NAL) and the Aeronautical Development Agency (ADA) in Bengaluru, India.
Youtube Recording: TBA.
June 25 (Talk 2): Multi-Agent Patrolling: Questions, Insights, and Contributions
June 25 (Talk 2): Multi-Agent Patrolling: Questions, Insights, and Contributions
Speaker: S Deepak Mallya (IIT Bombay)
Speaker: S Deepak Mallya (IIT Bombay)
Abstract: Multi-agent patrolling is a fundamental problem in robotics with applications ranging from campus and infrastructure surveillance to environmental monitoring and autonomous security systems. The objective is deceptively simple: coordinate a team of agents to repeatedly visit important locations in an environment. Yet, the problem gives rise to rich questions involving optimization, graph theory, task allocation, and long-term autonomous decision-making. This talk introduces the multi-agent patrolling problem, discusses the diverse formulations studied in the literature, and reviews the key approaches that have been proposed over the past two decades. Particular attention is given to several intriguing open questions concerning the existence, structure, and optimality of patrol strategies. The talk then presents research contributions that address some of these questions through a unified framework based on recurrent patrol strategies. The discussion covers both theoretical results on the nature of near-optimal solutions and practical algorithms for priority-based patrolling with multiple agents, highlighting how these results contribute to practical autonomous surveillance systems.
Abstract: Multi-agent patrolling is a fundamental problem in robotics with applications ranging from campus and infrastructure surveillance to environmental monitoring and autonomous security systems. The objective is deceptively simple: coordinate a team of agents to repeatedly visit important locations in an environment. Yet, the problem gives rise to rich questions involving optimization, graph theory, task allocation, and long-term autonomous decision-making. This talk introduces the multi-agent patrolling problem, discusses the diverse formulations studied in the literature, and reviews the key approaches that have been proposed over the past two decades. Particular attention is given to several intriguing open questions concerning the existence, structure, and optimality of patrol strategies. The talk then presents research contributions that address some of these questions through a unified framework based on recurrent patrol strategies. The discussion covers both theoretical results on the nature of near-optimal solutions and practical algorithms for priority-based patrolling with multiple agents, highlighting how these results contribute to practical autonomous surveillance systems.
Speaker Bio: Deepak Mallya is a PhD in Systems and Control currently working at dotRobotics, where he is leading efforts to productize an existing robotics prototype into a scalable and deployment-ready product. His professional interests include multi-agent systems, robotics, autonomous systems, and distributed intelligence. Beyond his technical work, he is deeply interested in Indic knowledge systems and Hindustani classical music. His work reflects a blend of rigorous engineering, product thinking, and a continuing engagement with India's intellectual and cultural traditions.
Youtube Recording: TBA.
July 2026
July 2026
July 2: From Theory to Practice: Bringing Robots into the Real World
July 2: From Theory to Practice: Bringing Robots into the Real World
Speaker: Dr. Vighnesh Vatsal (TCS Research, India)
Speaker: Dr. Vighnesh Vatsal (TCS Research, India)
Abstract: Robotics is hard. Not because we have too many gaps in our theories. But because it tries to bridge the gap between the computational world and our physical world-- going from bits to atoms. Our models and simulators cannot capture reality in all its complexities, so roboticists are always working partially in the dark. In this talk, we will explore how robotics can be applied to real-world scenarios even with these inherent shortcomings-- how we optimize for things under our control, and account for those that we cannot. As examples, we will look at industrial mobile manipulation, human-robot interaction in the form of wearable devices, humanoid robots, and space robotics. After outlining the key technologies and practices used in these domains, we will discuss the path forward for robotics as a multidisciplinary field, and how you can contribute to it regardless of your specific area of expertise.
Abstract: Robotics is hard. Not because we have too many gaps in our theories. But because it tries to bridge the gap between the computational world and our physical world-- going from bits to atoms. Our models and simulators cannot capture reality in all its complexities, so roboticists are always working partially in the dark. In this talk, we will explore how robotics can be applied to real-world scenarios even with these inherent shortcomings-- how we optimize for things under our control, and account for those that we cannot. As examples, we will look at industrial mobile manipulation, human-robot interaction in the form of wearable devices, humanoid robots, and space robotics. After outlining the key technologies and practices used in these domains, we will discuss the path forward for robotics as a multidisciplinary field, and how you can contribute to it regardless of your specific area of expertise.
Speaker Bio: Dr. Vighnesh Vatsal is a Scientist at TCS Research, Tata Consultancy Services, working in the areas of robotics, AI and control. He received a Ph.D. from Cornell University in 2020 in Mechanical Engineering with a focus on Human-Robot Collaboration. He previously received B.Tech. and M.Tech. degrees in Mechanical Engineering from IIT Bombay with a focus on automation and control. His research interests span the full spectrum of robotics and automation - including mobile manipulation, grasping, reinforcement learning and applied machine learning, exoskeletons, and space robotics.
Youtube Recording: TBD
July 9: Human-Robot Interaction in Wearable Systems: Comfort, Adaptation, and Immersion
July 9: Human-Robot Interaction in Wearable Systems: Comfort, Adaptation, and Immersion
Speaker: Prof. Vineet Vashista (IIT Gandhinagar, India)
Speaker: Prof. Vineet Vashista (IIT Gandhinagar, India)
Abstract: Exoskeletons and exosuits, designed to assist or resist human movement, play a vital role in both rehabilitation and performance augmentation. Their effectiveness depends on transparent and comfortable human-robot interaction, where user adaptation to device dynamics is central. This talk presents insights from gait adaptation studies using cable-driven exoskeletons and passive exosuits, highlighting how design choices such as cable routing and device-to-body forces influence outcomes. It also explores immersive interventions that integrate virtual reality with wearable systems to enhance intent detection and user engagement. Ongoing work at the Human-Centered Robotics Lab at IIT Gandhinagar demonstrates how multidisciplinary approaches can advance wearable robotics to restore mobility and augment human performance.
Abstract: Exoskeletons and exosuits, designed to assist or resist human movement, play a vital role in both rehabilitation and performance augmentation. Their effectiveness depends on transparent and comfortable human-robot interaction, where user adaptation to device dynamics is central. This talk presents insights from gait adaptation studies using cable-driven exoskeletons and passive exosuits, highlighting how design choices such as cable routing and device-to-body forces influence outcomes. It also explores immersive interventions that integrate virtual reality with wearable systems to enhance intent detection and user engagement. Ongoing work at the Human-Centered Robotics Lab at IIT Gandhinagar demonstrates how multidisciplinary approaches can advance wearable robotics to restore mobility and augment human performance.
Speaker Bio: Dr. Vineet Vashista is an Associate Professor at the Indian Institute of Technology Gandhinagar, where he leads the Human-Centered Robotics (HCR) Laboratory. He earned his Ph.D. in Mechanical Engineering from Columbia University in 2015 and a Master’s in Mechanical Design from IIT Delhi in 2008. His research focuses on human-centered robotic technologies inspired by human motor adaptation, with applications in rehabilitation, wearable robotics, and performance augmentation. Supported by major national and international funding agencies, his work investigates neuro-musculoskeletal processes to design devices that measure and influence human movement. Dr. Vashista is a recipient of the Haryana Yuva Vigyan Ratna Award (2022) and a member of INYAS (2023).
Youtube Recording: TBD
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