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Regular Papers

Volume 112, Issue 3

Scanning the Issue

An overview of advancements in multimotor drives: structural diversity, advanced control, specific technical challenges, and solutions.

By C. Gong, Y. R. Li, and N. R. Zargari

This article reviews the advances in multimotor drives from multiple perspectives and provides insights into future directions and opportunities.

Internet-Based Social Engineering Psychology, Attacks, and Defenses: A Survey

By T. T. Longtchi, R. Montañez Rodriguez, L. Al-Shawaf, A. Atyabi, and S. Xu

This article systemizes Internet-based social engineering attacks through a psychological lens and investigates why current defenses have limited success. It also provides a roadmap for future research studies.

Drawing the Boundaries Between Blockchain and Blockchain-Like Systems: A Comprehensive Survey on Distributed Ledger Technologies

By B. Bellaj, A. Ouaddah, E. Bertin, N. Crespi, and A. Mezrioui

This article aims to establish clearer distinctions between different categories within the distributed ledger technology ecosystem by providing a structured framework.

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• Sponsor: The Sponsor of the Award is The Institute of Electrical and Electronics Engineers, Incorporated (“IEEE”) on behalf of IEEE Access , 445 Hoes Lane, Piscataway, NJ 08854-4141 USA (“Sponsor”).
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• How to Enter: This Award opens on January 1, 2024 at 12:00 AM ET and all entries must be received by 11:59 PM ET on June 30, 2024 (“Promotional Period”).

Entrant must submit a video with an article submission to IEEE Access . The video submission must clearly be relevant to the submitted manuscript.  Only videos that accompany an article that is accepted for publication in IEEE Access will qualify.  The video may be simulations, demonstrations, or interviews with other experts, for example.  Your video file should not exceed 100 MB.

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• The IEEE Author Portal : Entrants can upload their video entries while submitting their article through the IEEE Author Portal submission site .
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Entries must be original. Entries that copy other entries, or the intellectual property of anyone other than the Entrant, may be removed by Sponsor and the Entrant may be disqualified. Sponsor reserves the right to remove any entry and disqualify any Entrant if the entry is deemed, in Sponsor’s sole discretion, to be inappropriate.

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• Number of Prizes Available, Prizes, Approximate Retail Value and Odds of winning Prizes: Two (2) promotional prizes of $350 USD Amazon gift cards. One (1) grand prize of a $500 USD Amazon gift card. Prizes will be distributed to the winners after the selection of winners is announced. Odds of winning a prize depend on the number of eligible entries received during the Promotional Period. Only the corresponding author of the submitted manuscript will receive the prize.

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Publications, trustworthy 6g, publication date, manuscript submission deadline, 1 august 2024, call for papers.

Submit a Paper

Currently, IMT 2030 proposes trustworthiness as a new characteristic in the 6G vision, and Huawei mentions native trustworthiness for 6G technology and requirements. In fact, various standard organizations, such as 3GPP, ETSI, and IEEE have been working on trustworthiness topics. Meanwhile, the world's major communications companies, including China Mobile, Nokia, Ericsson, etc., have clearly stated the need for 6G trustworthiness in their 6G initiatives, proposals, and white papers. Furthermore, many researchers have published technical work on the definition, generation, protection, and optimization of trustworthiness. All of these indicate that trustworthiness will become an indispensable key feature in 6G.

First, as a new characteristic, how to define trustworthiness in 6G is an open issue. Can trustworthiness be straightforwardly equated with safety, security, privacy, reliability, and resilience, or is it characterized by behaving exactly as expected? Second, what key performance indicators are typically suitable for trustworthiness and how to rate trustworthiness precisely. These challenges have not been well addressed. Lastly, due to the diversity of the concept of trustworthiness in academia and industry, there are many views on theories, technologies, and applications for trustworthiness in 6G, which have not yet reached a consensus to form a clear and systematic guide for the coming 6G era. The objective of this Special Issue (SI) is to enable both academic and industry researchers to present their research on trustworthy 6G. The SI also seeks to identify new application areas within this developing field and strongly encourages original research articles related to this topic, as well as high-quality review articles describing the current state of the art. Potential topics of interest include but are not limited to the following:

• Definitions of trustworthiness in relation to safety, security, privacy, reliability, robustness, resilience, explainability, accountability, integrity, availability, etc.
• Traditional technologies, information theory, cryptography, zero-trust, zero-knowledge proofs, artificial intelligence, etc., for trustworthy 6G.
• New capabilities of Account, Authorization, Authentication, Audit, etc. for trustworthy 6G.
• Integration of trustworthiness at the physical, data link, and network layers of 6G.
• Trustworthy environment engineering in 6G.
• Testing and evaluating trustworthiness in 6G.
• Industry and standardization efforts on trustworthy 6G.

Submission Guidelines

Prospective authors should prepare their submissions in accordance with the rules specified in the "Information for Authors" of the IEEE Wireless Communications guidelines .

Authors should submit a PDF version of their complete manuscript to  Manuscript Central . The timetable is as follows:

Important Dates

Manuscript Submission Deadline:  1 August 2024 Initial Decision Date: 1 October 2024 Revised Manuscript Due: 1 November 2024 Final Decision Date: 1 December 2024 Final Manuscript Due:  1 February 2025 Publication Date:  April 2025

Guest Editors

Bin Cao Beijing University of Posts and Telecommunications, China

Abbas Kiani Futurewei Technologies Inc., USA

Lan Zhang Clemson University, USA

Weizhi Meng Technical University of Denmark, Denmark

NVIDIA Presents New Robotics Research on Geometric Fabrics, Surgical Robots, and More at ICRA

During the IEEE International Conference on Robotics and Automation (ICRA) May 13-17 in Yokohama, Japan, many people will be discussing geometric fabrics. That topic is the subject of one of seven papers submitted by members of the NVIDIA Robotics Research Lab , along with collaborators, and featured at ICRA this week.  

What are geometric fabrics? 

In robotics, trained policies are approximate by nature. They usually do the right thing, but sometimes, they move the robot too fast, collide with things, or jerk the robot around. There is no guarantee of what may occur. 

So, any time that someone deploys trained policies and especially reinforcement learning-trained policies on a physical robot, they use a layer of low-level controllers to intercept the commands from the policy. Then, they translate those commands so that they satisfy the limitations of the hardware. 

When you’re training RL policies, run those controllers with the policy during training. The researchers determined that a unique value that could be supplied with their GPU-accelerated RL training tools was to vectorize those controllers so they’re available both during training and deployment. That’s what this research does.

For example, companies working on humanoid robots may show demos with low-level controllers that balance the robot but also keep the robot from running its arms into its own body. 

The controllers the researchers chose to vectorize are from a past line of work on geometric fabrics. The paper, Geometric Fabrics: Generalizing Classical Mechanics to Capture the Physics of Behavior , won a best paper award at last year’s ICRA. 

DeXtreme policies

The in-hand manipulation tasks that the researchers address in this year’s paper also come from a well-known line of research on DeXtreme .

In this new work, the researchers merge those two lines of research to train DeXtreme policies over the top of vectorized geometric fabric controllers. This keeps the robot safer, guides policy learning through the nominal fabric behavior, and systematizes sim2real training and deployment to get one step closer to using RL tooling in production settings.

This creates a foundational infrastructure enabling the researchers to quickly iterate to get the domain randomization right during training for successful sim2real deployment. For instance, by iterating quickly between training and deployment, they could adjust the fabric structure and add substantial random perturbation forces during training to achieve a level of robustness far superior to previous work.

In the prior DeXtreme work, the real-world experiments proved extremely hard on the physical robot, wearing down the motors and sensors and changing the behavior of underlying control through the course of experimentation. At one point, the robot broke down and started smoking! 

But with geometric fabric controllers underlying the policy and protecting the robot, the researchers found that they could be much more liberal in deploying and testing policies without worrying about the robot destroying itself.

For more information, see Geometric Fabrics: A Safe Guiding Medium for Policy Learning or watch the DeXtreme example videos . 

More robotics research at ICRA

Other noteworthy papers submitted this year include the following: 

Out of Sight, Still in Mind

• Point Cloud World Model

The SynH2R authors propose a framework to generate realistic human grasping motions suitable for training a robot. For more information, see SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers .

The RDMemory authors test a robotic arm’s reaction to things previously seen but then occluded from view to ensure that it responds reliably in various environments. This work was done in both simulation and in real-world experiments.

For more information, see Out of Sight, Still in Mind: Reasoning and Planning About Unobserved Objects With Video Tracking Enabled Memory Models or watch the RDMemory example videos . 

Point Cloud World Models

The Point Cloud World Models researchers set up a novel Point Cloud World Model (PCWM) and point cloud-based control policies that were shown to improve performance, reduce learning time, and increase robustness for robotic learners.

For more information, see Point Cloud Models Improve Visual Robustness in Robotic Learners .

The SKT-Hang authors look at the problem of how to use a robot to hang up a wide variety of objects on different supporting structures (Figure 1). While this might seem like an easy problem to solve, the variations in both the shapes of the objects as well as the supporting structures pose multiple challenges for the robot to overcome.

For more information, see SKT-Hang: Hanging Everyday Objects via Object-Agnostic Semantic Keypoint Trajectory Generation and the /HCIS-Lab/SKT-Hang GitHub repo.

Robots with surgical precision

Several new research papers have applications for use in hospital surgical environments.

ORBIT-Surgical

ORBIT-Surgical is a physics-based surgical robot simulation framework with photorealistic rendering powered by NVIDIA Isaac Sim on the NVIDIA Omniverse platform. 

It uses GPU parallelization to train reinforcement learning and imitation learning algorithms that facilitate the study of robot learning to augment human surgical skills. It also enables realistic synthetic data generation for active perception tasks. The researchers demonstrate using ORBIT-Surgical sim-to-real transfer of learned policies onto a physical dVRK robot.

The underlying robotics simulation application for ORBIT-Surgical will be released as a free, open-source package upon publication. 

For more information, see ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity .

The DefGoalNet paper focuses on shape servoing , which is a robotic task dedicated to controlling objects to create a specific goal shape. For more information, see DefGoalNet: Contextual Goal Learning From Demonstrations for Deformable Object Manipulation .

Meet NVIDIA Robotics partners at ICRA 

NVIDIA robotics partners are showing off their latest developments at ICRA. 

Zürich-based ANYbotics presents its ANYmal Research, which provides a complete software package that grants users access to low-level controls down to the ROS system. ANYmal Research is a community of hundreds of researchers working in top robotics research centers, including the AI Institute, ETH Zürich, and the University of Oxford. (Booth IC010)

Munich-based Franka Robotics highlights its work with NVIDIA Isaac Manipulator , an NVIDIA Jetson -based AI companion to power robot control and the Franka toolbox for Matlab. (Booth IC050)

Enchanted Tools shows off its Jetson-powered Mirokaï robots. (Booth IC053)

The NVIDIA Robotics Research Lab is a Seattle-based center of excellence focused on robot manipulation, perception, and physics-based simulation. It’s part of NVIDIA Research , which has more than 300 leading researchers around the globe, focused on topics spanning AI, computer graphics, computer vision, and self-driving cars.

Related resources

• GTC session: AI Robotics: Driving Innovation for the Future of Automation
• GTC session: Immersive Digital Assistants for Hybrid Teams: Weaving Generative AI into Collaborative XR for Manufacturing Planning
• GTC session: Next Phase of Industrial Robot Skills with AI
• SDK: Isaac Sim
• SDK: Isaac ROS
• Webinar: Enhancing Robotic Perception: Synthetic Data Generation using Omniverse Replicator

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