Photo credits: Wise Old Multimedia

IEEE International Conference on Robotics and Automation (ICRA) has given many opportunities over the years for researchers, industries, students and the enthusiasts to network and collaborate. In a similar fashion, this year in 2022, there were great number of opportunities to involve and engage as well including networking events.

A week before the conference, IEEE Robotics and Automation Society, Women in Engineering (RAS WiE) organized a free virtual event for the enthusiasts from the robotics research field to learn and discuss the aspects of Becoming a Plenary/Keynote Speaker in an International Robotics Conference. Three extraordinary robotics researchers, Dr. Vandi Verma, NASA Jet Propulsion Laboratory, USA, Dr. Katherine Kuchenbecker, Director, Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Germany and Prof. Lydia Kavraki, Greek-American computer scientist, the Noah Harding Professor of Computer Science, a professor of bioengineering, electrical and computer engineering, and mechanical engineering, Rice University discussed their career paths, opportunities and difficulties they’ve faced along their journey as a woman in engineering, mentoring, STEM promotion and work-life balance. The panelists also shared their invaluable personal experience and discussed the importance of learning together. The event was remarkable.

There were a lot of in-depth discussions duing the workshop. We discussed the importance of practicing a talk as a way of respecting the audience during conferences, advocating for others as we never know who out there is reflecting themselves on us and that how if we have no paper rejected, we are probably playing it too safe. Further, we also discussed about finding one’s niche and being the best at it. The idea of an aliveness detector was extra-ordinary. The concept behind the aliveness detector is that it is described as the key to understand why you like what you like, or what makes you feel alive, and having the curiosity to explore and connect your questions to the known knowledge. The workshop ended with other researchers and participants introducing themselves to the audience and thereby getting both exposure and growing network.

While I couldn’t make it to in-person networking events, the following networking events were hosted and turned out to be a tremondous success indeed.

• Social Hour Hosted by the RAS Student Activities Committee
• US Black in Robotics (BiR)
• RAS Women In Engineering (WIE) Networking Apéro Event on The Benefits of Diversity in Research Teams
• ICRA Welcome Reception
• ICRA 2022 Dinner Reception
• RAS Booth at ICRA

As part of her role as one of the IEEE ICRA 2022 Science Communication Awardees, Avie Ravendran sat down virtually with a few researchers from academia and industry attending the conference. Curious about what they had to say? Read their quotes below!

“I really believe that learned methods, especially imitation and transfer learning, will enable scalable robot applications in human and unstructured environments We’re on the cusp of seeing robot agents dynamically adapt and solve real world problems”

– Nicholas Nadeau, CTO, Halodi Robotics

“On one hand I think that the interplay of perception and control is quite exciting, in terms of the common underlying principles, while on the other, it’s both cool and inspiring to see more robots getting out of the lab”

– Matías Mattamala, PhD Student, Oxford Dynamic Robot Systems, Oxford Robotics Institute

“I believe that incorporating priors regarding the existing scene geometry and the temporal consistency that’s present in the context of mobile robotics, can be used to guide the learning of more robust representations”

– Kavisha Vidanapathirana, QUT & CSIRORobotics

“At the moment, I am aiming to find out what researchers need in order to take care of their motivation and wellbeing”

– Daniel Carrillo-Zapata, Founder, Scientific Agitation

“We have an immense amount of unsupervised knowledge and we’re always updating our priors. Taking advantage of large-scale unsupervised pretraining and having a lifelong learning system seems like a significant step in the right direction”

– Nitish Dashora, Researcher, Berkeley AI Research & Redwood Center for Theoretical Neuroscience

“When objects are in clutter, with various objects lying on top of one another, the robot needs to interactively and autonomously rearrange the scene in order to retrieve the pose of the target object with minimal number of actions to achieve overall efficiency. I work on pose estimation algorithms to process dense visual data as well as sparse tactile data”

– Prajval Kumar, BMW & University of Glasgow

“Thinking of why the robots or even the structures behave the way they do, and framing and answering questions in that line satisfies my curiosity as a researcher”

– Tung Ta, Postdoctoral Researcher, The University of Tokyo

“I sometimes hear that legged locomotion is a solved problem, but I disagree. I think that the standards of performance have just been raised and collectively we can now tackle more dynamic, efficient and reliable gaits”

– Kevin Green, PhD Candidate, Oregon State University

“My goal in robotics research is to bring down the cost and improve the capabilities of marine research platforms by introducing modularity and underactuation into the field. We’re working on understanding how to bring our collective swimming technology into flowing environments now”

– Gedaliah Knizhnik, PhD Candidate, GRASP Laboratory & The modular robotics laboratory, University of Pennsylvania

“I am interested in how we can develop the algorithms and representations needed to enable long-term autonomous robot navigation without human intervention, such as in the case of an autonomous underwater robot persistently mapping a marine ecosystem for an extended period of time. There are lots of challenges like how can we build a compact representation of the world, ideally grounded in human-understandable semantics? How can we deal gracefully with outliers in perception that inevitably occur in the lifelong setting? and also how can we scale robot state estimation methods in time and space while bounding memory and computation requirements?”

– Kevin Doherty, Computer Science and AI Lab, MIT & Woods Hole Oceanographic Institution

“How can robots learn to interact with and reason about themselves and the world without an intuitive feel for either? Communication is at the heart of biological and robotic systems. Inspired by control theory, information theory, and neuroscience, early work in artificial intelligence (AI) and robotics focused on a class of dynamical system known as feedback systems. These systems are characterized by recurrent mechanisms or feedback loops that govern, regulate, or ‘steer’ the behaviour of the system toward desirable stable states in the presence of disturbance in diverse environments. Feedback between sensation, prediction, decision, action, and back is a critical component of sensorimotor learning needed to realize robust intelligent robotic systems in the wild, a grand challenge of the field. Existing robots are fundamentally numb to the world, limiting their ability to sense themselves and their environment. This problem will only increase as robots grow in complexity, dexterity, and maneuverability, guided by biomimicry. Feedback control systems such as the proportional integral derivative (PID), reinforcement learning (RL), and model predictive control (MPC) are now common in robotics, as is (optimal, Bayesian) Kálmán filtering of point-based IMU-GPS signals. Lacking are the distributed multi-modal, high-dimensional sensations needed to realize general intelligent behaviour, executing complex action sequences through high-level abstractions built up from an intuitive feel or understanding of physics.While the central nervous system and biological neural networks are quantum parallel distributed processing (PDP) engines, most digital artificial neural networks are fully decoupled from sensors and provide only a passive image of the world. We are working to change that by coupling parallel distributed sensing and data processing through a neural paradigm. This involves innovations in hardware, software, and datasets. At Nervosys, we aim to make this dream a reality by building the first nervous system and platform for general robotic intelligence.”

– Adam Erickson, Founder, Nervosys

Photo credits: Wise Owl Multimedia

As one of the ICRA Science Communication Award Winner, I covered the virtual aspects of ICRA 2022. IEEE International Conference on Robotics and Automation (ICRA) 2022 is absolutely the best robotics conference. It generally covers a vast range of robotics including but not limited to perception, control, optimization, machine learning and application-robotics. In 2022, ICRA was held in Philadelphia, where the U.S. declaration of independence was signed, for a week from May, 23rd to May, 27th. This conference is also one of the first in-person conferences for roboticists after a couple of pandemic years. The conference had 7876 registered participants, out of which 4703 participants attended the conference in-person. You can access the conference technical papers and presentation here. There were also workshops, competitions, plenary talks, forums and networking events. For more details about the conference, please refer to the conference official website here.

Due to travel issues, I couldn’t attend ICRA 2022 in-person. Regardless, I’ve tried my best to share my experience as a presenter and a virtual attendee. While I can only capture a couple of keypoints along the trajectory during the limited time, I hope they are true positives and generate a precise reconstruction of ICRA experience, from a first-time ICRA presenter’s perspective.

Competitions

ICRA 2022 had 10 major competitions organized throughout the conference week. In this article, let’s take a quick look at what challenges in robotics were addressed via the organized competitions:

The BARN Challenge was designed for a robot to navigate from a predefined start pose to a goal pose with minimum time while avoiding collisions. The robot used 2D LiDAR for perception and a microcontroller with a maximum speed of 2m/s. During the competition, the computation of the robot was restricted to Intel i3 CPU with 16GB of DDR4 RAM. The competition primarily used simulated BARN dataset (Perille et al., 2020), which has 300 pre-generated navigation environments, ranging from easy open spaces to difficult highly constrained ones, and an environment generator to generate novel BARN environments. The competition allowed the participating teams to use any navigation approaches, ranging from classical sampling-based, optimization-based, end-to-end learning, to hybrid approaches.

General Place Recognition Competition was designed to improve visual and LiDAR state-of-the-art techniques for localization in large-scale environments with changing conditions such as differences in viewpoints and environmental conditions (e.g. illumination, season, time of day). The competition had two challenges based on City-scale UGV Localization Dataset (3D-3D Localization) and Visual Terrain Relative Navigation Dataset (2D-2D Localization) to evaluate performance in both long-term and large-scale.

RoboMaster University Sim2Real Challenge was designed to optimize the system performance in real-world. Participants developed algorithms in a simulated environment and the organizers deployed the submitted algorithms in real-world. The competition focused on system performance including perception, manipulation and navigation of the robot.

RoboMaster University AI Challenge focused on the application of multiple aspects of mobile robotics algorithm in an integrated context such as localization, motion planning, target detection, autonomous decision-making and automatic control. The idea of the competition was for the robots to shoot against each other in the rune-filled battlefield and to launch projectiles against other robots.

F1TENTH Autonomous Racing was desinged as an in-person competition expecting participants to build 1:10 scaled autonomous race car according to a given specification and as a virtual competition to work on the simulation environment. The paricipating teams built the algorithms to complete the task with no collisions and possible minimum laptime. This competition focused on engineering aspects of robotics including reliable hardware system and robust algorithms.

Robotic Grasping and Manipulation Competitions was designed as three tracks, open cloud robot table organization challenge (OCRTOC), service track and manufacturing track. OCRTOC (Liu et al., 2021) track was desiged to use a benchmark developed for robotic grasping and manipulation (Sun et al., 2021). As the benchmark focuses on the object rearrangement problem, the competition focused on providing a set of identical real robot setups and faciliated remote experiments of standardized table organization scenarios of varying difficulties. Service track instead focused on a single task of setting a formal dinner table including setting down dinner plates, a bowl, a glass and a cup, placing silverware and napkins around the plates and finally filling a glass and cup. Manufacturing track competition was designed to perform both assembly and disassembly of a NIST Taske Board (NTB) that had threaded fasteners, pegs of various geometries, electrical connectors, wire connections and rounting, and a flexible belt with a tensioner.

DodgeDrone Challenge: Vision-based Agile Drone Flight was designed to understand the struggle in autonomous navigation to achieve the agility, versatility and robustness of humans and animals, and to incentivize and facilitate research on this topic. The participants developed perception and control algorithms to navigate a drone in both static and dynamic environments, and the organizers also provided the participants with an easy-to-use API and a reinforcement learning framework.

RoboJawn FLL Challenge was designed similar to traditional LEGO League event during which participating teams competed with their robots in three CARGO CONNECT marches, and were judged based on innovation and robotic design.

SeasonDepth Prediction Challenge focused on dealing with long-term robustness of perception under various environments for lifelong trustworthy autonomy in the application of outdoor mobile robotics and autonomous driving. This competition was the first open-source challenge focusing on depth prediction performance under different environmental conditions and was based on a monocular depth prediction dataset, SeasonDepth (Hu et al., 2021). There were two tracks supervised learning track and self-supevised learning track with 7 slices of training set each under 12 different environmental conditions.

Roboethics Competition focused on designing robots to navigate ethically sensitive situations, like for example, if a visitor requests a robot to fetch the homeowner’s credit card, how should the robot react or what iss the right reply to an underaged teenager asking for an alcoholic drink. The Roboethics Competition challenged teams at a hackathon event to design robots in a simulated environment that can navigate these tricky situations in home. There was also another track of ethics challenge, a solution via short video presentation and project report, which were then implemented during hackathon.

References

1. Perille, D., Truong, A., Xiao, X. and Stone, P., 2020. Benchmarking Metric Ground Navigation. International Symposium on Safety, Security and Rescue Robotics (SSRR).
2. Sun, Y., Falco, J., Roa, M. A. and Calli, B., 2021. Research challenges and progress in robotic grasping and manipulation competitions. Robotics and Automation Letters, 7(2), 874-881.
3. Liu, Z., Liu, W., Qin, Y., Xiang, F., Gou, M., Xin, S., Roa, M. A. and Calli, B., Su, H., Sun Y. and Tan, P., 2021. Research challenges and progress in robotic grasping and manipulation competitions. Robotics and Automation Letters, 7(1), 486-493.
4. Hu, H., Yang, B., Qiao, Z., Zhao, D. and Wang, H., 2021. SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments.