Coffee with a Researcher (#ICRA2022)

by and
20 June 2022

share this:

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


Daniel Carrillo-Zapata was awared his PhD in swarm robotics at the Bristol Robotics Lab in 2020. He now fosters the culture of "scientific agitation" to engage in two-way conversations between researchers and society.
Daniel Carrillo-Zapata was awared his PhD in swarm robotics at the Bristol Robotics Lab in 2020. He now fosters the culture of "scientific agitation" to engage in two-way conversations between researchers and society.

Ahalya Ravendran is a doctoral student at the Australian Centre for Field Robotics, The University of Sydney, Australia.
Ahalya Ravendran is a doctoral student at the Australian Centre for Field Robotics, The University of Sydney, Australia.

Related posts :

Hot Robotics Symposium celebrates UK success

An internationally leading robotics initiative that enables academia and industry to find innovative solutions to real world challenges, celebrated its success with a Hot Robotics Symposium hosted across three UK regions last week.
25 June 2022, by

Researchers release open-source photorealistic simulator for autonomous driving

MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
22 June 2022, by

In this episode, Audrow Nash speaks to Maria Telleria, who is a co-founder and the CTO of Canvas. Canvas makes a drywall finishing robot and is based in the Bay Area. In this interview, Maria talks ab...
21 June 2022, by and

Seeing the robots at #ICRA2022 through the eyes of a robot

Accessbility@ICRA2022 and OhmniLabs provided three OhmniBots for the conference, allowing students, faculty and interested industry members to attend the expo and poster sessions.
17 June 2022, by

Communicating innovation: What can we do better?

The question on what role communications play in forming the perception of innovative technology was discussed in this workshop. Experts explained how the innovation uptake should be supported by effective communication of innovations: explaining the benefits, tackling risks and fears of the audiences, and taking innovation closer to the general public.
15 June 2022, by

Robots could help reaching UN goals of sustainable development

The use of robots plays an important role in reaching the sustainable development goals set out by the United Nations (17 SDGs). The International Federation of Robotics identified 13 SDGs, where robots help to create a better planet.

©2021 - ROBOTS Association


©2021 - ROBOTS Association