Learning companion robots for young children are increasingly adopted in informal learning environments. Although parents play a pivotal role in their children's learning, very little is known about how parents prefer to incorporate robots into their children's learning activities. We developed prototype capabilities for a learning companion robot to deliver educational prompts and responses to parent-child pairs during reading sessions and conducted in-home user studies involving 10 families with children aged 3-5. Our data indicates that parents want to work with robots as collaborators to augment parental activities to foster children's learning, introducing the notion of parent-robot collaboration. Our findings offer an empirical understanding of the needs and challenges of parent-child interaction in informal learning scenarios and design opportunities for integrating a companion robot into these interactions. We offer insights into how robots might be designed to facilitate parent-robot collaboration, including parenting policies, collaboration patterns, and interaction paradigms.

In this course, we will learn how a human-robot interaction course at an undergraduate liberal robots use computational models to have natural and intuitive social arts college. We provide a sample syllabus adapted from a previous interactions with humans.

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect >10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.

With the advent of the AI Act and other regulations, there is now an urgent need for algorithms that repair unfairness in training data. In this paper, we define fairness in terms of conditional independence between protected attributes ($S$) and features ($X$), given unprotected attributes ($U$). We address the important setting in which torrents of archival data need to be repaired, using only a small proportion of these data, which are $S|U$-labelled (the research data). We use the latter to design optimal transport (OT)-based repair plans on interpolated supports. This allows {\em off-sample}, labelled, archival data to be repaired, subject to stationarity assumptions. It also significantly reduces the size of the supports of the OT plans, with correspondingly large savings in the cost of their design and of their {\em sequential\/} application to the off-sample data. We provide detailed experimental results with simulated and benchmark real data (the Adult data set). Our performance figures demonstrate effective repair -- in the sense of quenching conditional dependence -- of large quantities of off-sample, labelled (archival) data.

We study online learning problems in constrained Markov decision processes (CMDPs) with adversarial losses and stochastic hard constraints. We consider two different scenarios. In the first one, we address general CMDPs, where we design an algorithm that attains sublinear regret and cumulative positive constraints violation. In the second scenario, under the mild assumption that a policy strictly satisfying the constraints exists and is known to the learner, we design an algorithm that achieves sublinear regret while ensuring that the constraints are satisfied at every episode with high probability. To the best of our knowledge, our work is the first to study CMDPs involving both adversarial losses and hard constraints. Indeed, previous works either focus on much weaker soft constraints--allowing for positive violation to cancel out negative ones--or are restricted to stochastic losses. Thus, our algorithms can deal with general non-stationary environments subject to requirements much stricter than those manageable with state-of-the-art algorithms. This enables their adoption in a much wider range of real-world applications, ranging from autonomous driving to online advertising and recommender systems.

Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning without available historical data. Compared with its counterpart (replay-based CIL) that stores historical samples, the EFCIL suffers more from forgetting issues under the exemplar-free constraint. In this paper, inspired by the recently developed analytic learning (AL) based CIL, we propose a representation enhanced analytic learning (REAL) for EFCIL. The REAL constructs a dual-stream base pretraining (DS-BPT) and a representation enhancing distillation (RED) process to enhance the representation of the extractor. The DS-BPT pretrains model in streams of both supervised learning and self-supervised contrastive learning (SSCL) for base knowledge extraction. The RED process distills the supervised knowledge to the SSCL pretrained backbone and facilitates a subsequent AL-basd CIL that converts the CIL to a recursive least-square problem. Our method addresses the issue of insufficient discriminability in representations of unseen data caused by a frozen backbone in the existing AL-based CIL. Empirical results on various datasets including CIFAR-100, ImageNet-100 and ImageNet-1k, demonstrate that our REAL outperforms the state-of-the-arts in EFCIL, and achieves comparable or even more superior performance compared with the replay-based methods.

In contrast, Robot Imitation Learning (IL) is a widely used method for training Imitation Learning (IL) aims to empower end-users to teach robots robots to perform manipulation tasks that involve mimicking skills and behaviors through demonstrations, showing promising human demonstrations to acquire skills. However, its practicality results in controlled laboratory environments [3, 4, 11, 13]. However, has been limited due to its requirement that users be trained current methods for collecting demonstrations require users in operating real robot arms to provide demonstrations. This paper to be acquainted with the operation of specific controllers or engage presents an innovative solution: an Augmented Reality (AR)- in contact-based kinesthetic teaching on real robot arms [6, 16, 18], assisted framework for demonstration collection, empowering nonroboticist thereby impeding the widespread application of IL. users to produce demonstrations for robot IL using devices To streamline demonstration collection for non-roboticists, it like the HoloLens 2. Our framework facilitates scalable and is crucial to address two issues: 1) non-expert users typically lack diverse demonstration collection for real-world tasks. We validate understanding of robot arm controllers, and 2) non-roboticists may our approach with experiments on three classical robotics tasks: face limited access to real robot arms due to their high cost and the reach, push, and pick-and-place. The real robot performs each task specialized nature of robot manipulators. While virtual reality (VR) successfully while replaying demonstrations collected via AR. has been used for addressing these challenges [8, 21], it requires

In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, nonprojective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory (SDRT), the Penn Discourse Treebank (PDTB) and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics and applications for data in our framework.

Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However, it often suffers inferior performance compared to the feature knowledge distillation. In this paper, we argue that existing logit-based methods may be sub-optimal since they only leverage the global logit output that couples multiple semantic knowledge. This may transfer ambiguous knowledge to the student and mislead its learning. To this end, we propose a simple but effective method, i.e., Scale Decoupled Distillation (SDD), for logit knowledge distillation. SDD decouples the global logit output into multiple local logit outputs and establishes distillation pipelines for them. This helps the student to mine and inherit fine-grained and unambiguous logit knowledge. Moreover, the decoupled knowledge can be further divided into consistent and complementary logit knowledge that transfers the semantic information and sample ambiguity, respectively. By increasing the weight of complementary parts, SDD can guide the student to focus more on ambiguous samples, improving its discrimination ability. Extensive experiments on several benchmark datasets demonstrate the effectiveness of SDD for wide teacher-student pairs, especially in the fine-grained classification task. Code is available at: https://github.com/shicaiwei123/SDD-CVPR2024

Human-robot teams involve humans and robots collaborating to achieve tasks under various environmental conditions. Successful teaming will require robots to adapt autonomously to a human teammate's internal state. An important element of such adaptation is the ability to estimate the human teammates' workload in unknown situations. Existing workload models use machine learning to model the relationships between physiological metrics and workload; however, these methods are susceptible to individual differences and are heavily influenced by other factors. These methods cannot generalize to unknown tasks, as they rely on standard machine learning approaches that assume data consists of independent and identically distributed (IID) samples. This assumption does not necessarily hold for estimating workload for new tasks. A survey of non-IID machine learning techniques is presented, where commonly used techniques are evaluated using three criteria: portability, model complexity, and adaptability. These criteria are used to argue which techniques are most applicable for estimating workload for unknown tasks in dynamic, real-time environments.