United States

In real-world scenarios, robots come closer to humans in many applications, sharing the same workspace or even manipulating the same objects. To ensure safe and intuitive collaboration, it is crucial to have an accurate knowledge of the human&#39;s 3D position in space, which should be estimated with high precision, high frequency and low latency. However, individual sensors such as inertial measurement units (IMUs) or cameras cannot meet all requirements for reliable human pose estimation under conditions such as long operating times, large distances and occlusions. In this study, we highlight the limitations of different visual pose methods and present a fused approach for real-time estimation of the 3D position of the human pelvis using machine learning-based visual pose from a monocular camera and an IMU sensor suit. The multimodal fusion is based on the Invariant Extended Kalman filter (InEKF) on Lie Groups, which fuses drift-free visual poses with high-frequency inertial measurements in a loosely-coupled manner. The evaluation is performed on a recorded dataset of multiple subjects performing various experimental scenarios. The results show that the fused approach can increase the accuracy and robustness of the estimates, taking a step closer towards smooth human-robot collaboration.

IEEE RO-MAN 2024

Fusion of Inertial Sensor Suit and Monocular Camera for 3D Human Pelvis Pose Estimation

imu sensor suit

visual pose

3d human pose estimation

kalman filter

sensor fusion

machine learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

In real-world scenarios, robots come closer to humans in many applications, sharing the same workspace or even manipulating the same objects. To ensure safe and intuitive collaboration, it is crucial to have an accurate knowledge of the human's 3D position in space, which should be estimated with high precision, high frequency and low latency. However, individual sensors such as inertial measurement units (IMUs) or cameras cannot meet all requirements for reliable human pose estimation under conditions such as long operating times, large distances and occlusions. In this study, we highlight the limitations of different visual pose methods and present a fused approach for real-time estimation of the 3D position of the human pelvis using machine learning-based visual pose from a monocular camera and an IMU sensor suit. The multimodal fusion is based on the Invariant Extended Kalman filter (InEKF) on Lie Groups, which fuses drift-free visual poses with high-frequency inertial measurements in a loosely-coupled manner. The evaluation is performed on a recorded dataset of multiple subjects performing various experimental scenarios. The results show that the fused approach can increase the accuracy and robustness of the estimates, taking a step closer towards smooth human-robot collaboration.

technical paper

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Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

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Complex manipulation tasks often require robots with complementary capabilities to collaborate. We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) focused on task allocation and long-horizon object manipulation based on human language instructions in a tabletop setting. LEMMA features 8 types of procedurally generated tasks with varying degree of complexity, some of which require the robots to use tools and pass tools to each other. For each task, we provide 800 expert demonstrations and human instructions for training and evaluations. LEMMA poses greater challenges compared to existing benchmarks, as it requires the system to identify each manipulators limitations and assign sub-tasks accordingly while also handling strong temporal dependencies in each task. To address these challenges, we propose a modular hierarchical planning approach as a baseline. Our results highlight the potential of LEMMA for developing future language-conditioned multi-robot systems.

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A B-Spline Approach for Improved Environmental Awareness in Virtual Walking System Using Avatar Robot

Research on underwater human-robot teaming is particularly promising because of complementary sensory skills that may overcome unique challenges associated with the combination of limited connectivity and low visibility. Nevertheless, testing human-robot collaboration under water, especially in complex, real-world scenarios, poses severe safety, cost, and time constraints that significantly hinder academic research in this space. This paper presents a novel cyber-physical extended-reality (XR) testbed, referred to as Underwater Real-Time Human Autonomous Systems Collaboration (UnRealTHASC), designed to enable human-robot interactions in simulated photorealistic environments with mixed real and virtual wearables, and advanced autonomous underwater sensors in-the-loop. Novel sensor interfaces are designed to integrate real and virtual sensors for measuring physiological and cognitive human states underpinning decision-making abilities. Physics-based human and robot motion models are developed along with new sensor simulations in order to capture the couplings between underwater behaviors and perception based on measurements from optical and sonar sensors. Real-time data acquisition pipelines are created to access and share data from both real and virtual sensors and robots, such that new methods for online planning and collaboration may be tested via human-in-the-loop demonstrations.

UnRealTHASC – a Cyber-Physical XR Testbed for Underwater Real-Time Human Autonomous Systems Collaboration

Quadruped robots are showing impressive abilities to navigate the real world. If they are to become more integrated into society, social trust in interactions with humans will become increasingly important. Additionally, robots will need to be adaptable to different humans based on individual preferences. In this work, we study the social interaction task of learning optimal handshakes for quadruped robots based on user preferences. While maintaining balance on three legs, we parameterize handshakes with a Central Pattern Generator consisting of an amplitude, frequency, stiffness, and duration. Through 10 binary choices between handshakes, we learn a belief model to fit individual preferences for 25 different subjects. Our results show that this is an effective strategy, with 76% of users feeling happy with their identified optimal handshake parameters, and 20% feeling neutral. Moreover, compared with random and test handshakes, the optimized handshakes have significantly decreased errors in amplitude and frequency, lower Dynamic Time Warping scores, and improved energy efficiency, all of which indicate robot synchronization to the user's preferences

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User Decision Guidance with Selective Explanation Presentation from Explainable-AI

Apology behavior design is becoming important for social robots that work in daily environments because of their widespread use. Robotics researchers have reported that multiple robots can effectively achieve more acceptance and trust in apology situations. Unfortunately, such effects have only been confirmed in a single country: Japan. Some studies investigated cultural differences in apologies between Japan and other cultures and reported how the former influences the perceived function and meaning of apologies. Therefore, we conducted a web-based survey to investigate whether using multiple robots in apology situations is effective in another country. We compared such perceived feelings as forgiveness and trust toward a robots apologies between Japan and the U.S. by using the visual stimuli of one and two robots. The experiment results showed that U.S. people felt that multiple robot apologies are more acceptable than apologies from just one robot, similar to results with Japanese participants. Perceived trust did show a different phenomenon between the two countries.

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