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1. Humans Need Augmented Feedback to Physically Track Non-Biological Robot Movements

   https://doi.org/10.1109/ICRA48891.2023.10161075  

   Edraki, Mahdiar; Maurice, Pauline; Sternad, Dagmar (May 2023, IEEE International Conference on Robotics and Automation)

   An important component for the effective collaboration of humans with robots is the compatibility of their movements, especially when humans physically collaborate with a robot partner. Following previous findings that humans interact more seamlessly with a robot that moves with humanlike or biological velocity profiles, this study examined whether humans can adapt to a robot that violates human signatures. The specific focus was on the role of extensive practice and realtime augmented feedback. Six groups of participants physically tracked a robot tracing an ellipse with profiles where velocity scaled with the curvature of the path in biological and nonbiological ways, while instructed to minimize the interaction force with the robot. Three of the 6 groups received real-time visual feedback about their force error. Results showed that with 3 daily practice sessions, when given feedback about their force errors, humans could decrease their interaction forces when the robot’s trajectory violated human-like velocity patterns. Conversely, when augmented feedback was not provided, there were no improvements despite this extensive practice. The biological profile showed no improvements, even with feedback, indicating that the (non-zero) force had already reached a floor level. These findings highlight the importance of biological robot trajectories and augmented feedback to guide humans to adapt to non-biological movements in physical human-robot interaction. These results have implications on various fields of robotics, such as surgical applications and collaborative robots for industry. 

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2. Collaborative Learning in Cloud-based Virtual Computer Labs

   https://doi.org/10.1109/FIE.2018.8659018  

   Hu, Xiaolin; Le, Hai; Bourgeois, Anu G.; Pan, Yi (October 2018, 2018 IEEE Frontiers in Education Conference (FIE))

   This Innovative Practice Work-In-Progress paper presents a collaborative virtual computer lab (CVCL) environment to support collaborative learning in cloud-based virtual computer labs. With advances of cloud computing and virtualization technologies, a new paradigm of virtual computer labs has emerged, where students carry out labs on virtualized resources remotely through the Internet. Virtual computer labs bring advantages, such as anywhere, anytime, on-demand access of specialized software and hardware. However, with current implementations, it also makes it difficult for students to collaborate, due to the fact that students are assigned separated virtual working spaces in a remote-accessing environment and there is a lack of support for sharing and collaboration. To address this issue, we develop a CVCL environment that allows students to reserve virtual computers labs with multiple participants and support remote real-time collaboration among the participants during a lab. The CVCL environment will implement several well-defined collaborative lab models, including shared remote collaboration, virtual study room, and virtual tutoring center. This paper describes the overall architecture and main features of the CVCL environment and shows preliminary results. 

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3. Inferring Relational Potentials in Interacting Systems

   Comas, A.; Du, Y.; Fernandez Lopez, C.; Ghimire, S.; Sznaier, M.; Tenenbaum, J. B.; Camps, O. (January 2023, Proceedings of Machine Learning Research)

   Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact robustly in the real world, it is thus important to be able to infer the precise interactions governing such systems. Existing approaches typically discover such interactions by explicitly modeling the feed-forward dynamics of the trajectories. In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory. NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed. We illustrate that with these representations NIIP displays unique capabilities in test-time. First, it allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting. Additionally, it allows adding external hand-crafted potentials at test-time. Finally, NIIP enables the detection of out-of-distribution samples and anomalies without explicit training. 

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4. Inferring Relational Potentials in Interacting Systems

   Comas, A.; Du, Y.; Fernandez Lopez, C.; Ghimire, S.; Sznaier, M.; Tenenbaum, J.B.; Camps, O. (July 2023, Proceedings of Machine Learning Research)

   Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact robustly in the real world, it is thus important to be able to infer the precise interactions governing such systems. Existing approaches typically dis- cover such interactions by explicitly modeling the feed-forward dynamics of the trajectories. In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory. NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed. We illustrate that with these representations NIIP displays unique capabilities in test-time. First, it allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting. Additionally, it allows adding external hand-crafted potentials at test-time. Finally, NIIP enables the detection of out-of-distribution samples and anomalies without explicit training. 

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5. Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions

   Xie, Tian; Zhang, Xueru (November 2024, Advances in Neural Information Processing Systems)

   As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Humans, as strategic agents, continuously adapt their behaviors in response to the learning system. As populations change dynamically, ML systems may need frequent updates to ensure high performance. However, acquiring high-quality human-annotated samples can be highly challenging and even infeasible in social domains. A common practice to address this issue is using the model itself to annotate unlabeled data samples. This paper investigates the long-term impacts when ML models are retrained with model-annotated samples when they incorporate human strategic responses. We first formalize the interactions between strategic agents and the model and then analyze how they evolve under such dynamic interactions. We find that agents are increasingly likely to receive positive decisions as the model gets retrained, whereas the proportion of agents with positive labels may decrease over time. We thus propose a refined retraining process to stabilize the dynamics. Last, we examine how algorithmic fairness can be affected by these retraining processes and find that enforcing common fairness constraints at every round may not benefit the disadvantaged group in the long run. Experiments on (semi-)synthetic and real data validate the theoretical findings. 

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