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We propose a systematic approach to regularize RNN-based sequential prediction by incorporating domain knowledge with logic constraints. We apply two methods - adding a layer after the hidden unit and combining class probabilities to improve the standard sequential prediction model. We confirm the effectiveness of our methods on surgical activity detection.

IEEE RO-MAN 2021

Physiological Data-Based Evaluation of a Social Robot Navigation System

surgical robotic activity recognition

logic constraints

sequential predictions

regularization

markov logic network

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

technical paper

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

In this paper, we conduct a longitudinal user study to investigate the differences in interactions participants had with an experienced human coach (HC) delivering mindfulness training with the interactions another group of participants had with a Robot Coach (RC) teleoperated by the HC. We aim to gain an understanding of the
evolving user perceptions towards the coaches (HC and RC), which can, in turn, help us design effective strategies to develop autonomous robot coaches that can deliver mental wellbeing interventions.

Towards a context-based Bayesian recognition of transitions in locomotion activities

This trial tested a social robot to help deliver a low-intensity problem-solving session for students around study-related issues. In the trial, 72 students reported good ratings for emotional reaction to the robot, perceived utility, intention to use the robot again, confidence to use the robot, and perceived helpfulness from the robot. One week later, most students reported that the robot session helped them to fix their chosen issue, and that they used at least one idea from the session.

A Robot-Delivered Program for Low-Intensity Problem-Solving Therapy for Students in Higher Education

This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp. This architecture allows the approach to rely on traded control instead of direct control: we use hand gestures to specify the goal objects for a sequential manipulation task, the robot then autonomously generates a grasping or a retrieving motion using trajectory optimization. The perception module relies on the model-based tracker to precisely track the 6D pose of the objects and makes use of a state of the art learning-based object detection and segmentation method, to initialize the tracker by automatically detecting objects in the scene. Goal objects are identified from user hand gestures using a trained a multi-layer perceptron classifier. After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i.e., one that does not use prediction) which shows that using intent 
prediction allows to bring down the overall task execution time.

A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures

Reconfigurable spherical robot No.3 can transform from the spherical shape into 3 legs walking using linear actuator. No.3 can walk on flat floor and cross barrier using butterfly walking gait and crawl-kick walking gait.The butterfly walking gait can achieve speed of 5.33 cm/s for flat floor and 1.57 cm/s for crossing the barrier. The new crawl-kick walking gait can achieve speed of 6.15 cm/s for flat floor and 1.51 cm/s crossing the barrier. No.3 can complete function of autonomous transformation, movement on flat floor and cross barrier using 3 legs walking gait. 


A Three-Legged Reconfigurable Spherical Robot No.3

We propose emotional music prosody can play a key role in improving the interaction between humans and groups of robots by modifying the level of entitativity. We were able to show with significant results that the use of musical prosody improved likeability and trust over soundless gestures for groups of robots. We also demonstrate that is able to alter the level of entitativity perceived by external observers. Finally, our results indicate a complex relationship between entitativity and common HRI metrics.

Emotion Musical Prosody for Robotic Groups and Entitativity

This project aims at designing a Series Elastic Actuator for an active upper limb exoskeleton to be used directly in the workplace. The design we studied is based on linear springs and developed a theoretical method that allowed us to select the springs based on the application requirements. The higher purpose is to prevent work-related musculoskeletal diseases by assisting workers in their daily job, reducing muscular fatigue in the upper limb when performing tasks involving arm elevation and weight-lifting.

Tell me more! A Robot’s Struggle to Achieve Artificial Awareness

Interpreting spatial language not only adds a perceptual modality for robots, but also reduces the barrier of interfacing with humans. However, spatial language is inherently subjective and ambiguous. We thus consider spatial language as stochastic observations. We propose SLOOP (Spatial Language Object-Oriented POMDP), a new framework for partially observable decision making with a probabilistic observation model for spatial language, and apply it to object search in city-scale environments. Results demonstrate successful leverage of spatial language for faster target search.

Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

We present a method for proactive approaching of humans for human-robot cooperative tasks. The proposed method can deal robustly with the uncertainties in the robot’s perception while also ensuring socially acceptable behavior.

Pedestrian Density-Based Path Recognition and Risk Prediction for Autonomous Vehicles

RRT-SMP demonstrates a method of increasing the safety of social interactions produced by a robot's navigation system by dynamically biasing the distribution of a set of motion primitives with input from a social intention model.

Towards Personalized Interaction and Corrective Feedback of a Socially Assistive Robot for Post-Stroke Rehabilitation Therapy

We present a new approach to human-aware robot navigation, which extends our previous proximity-based navigation framework by introducing visibility and predictability as new parameters. We derived these parameters from a user study and incorporated them into a cost function, which models the user’s discomfort with respect to a relative robot position based on proximity, visibility, predictability, and work efficiency.

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Physiological Data-Based Evaluation of a Social Robot Navigation System

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