Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies

Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework that incorporates Bayesian variational inference for learning flexible non-parametric multi-action policies, while simultaneously robustifying the policies against sources of error, by introducing and optimizing disturbances to create a richer demonstration dataset. This combinatorial approach forces the policy to adapt to challenging situations, enabling stable multi-action policies to be learned efficiently. The effectiveness of our proposed method is evaluated through simulations and real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm. Results show that, through improved flexibility and robustness, the learning performance and control safety are better than comparison methods.Comment: 7 pages, Accepted by the 2021 International Conference on Robotics and Automation (ICRA 2021

Effects of Teaching Methods on Swimming Skill Acquisition in Children with Developmental Coordination Disorder

This study compared the delivery of “verbal and visual” with “verbal, visual and tactile” swimming instruction to small groups of DCD pupils for front crawl and backstroke performance across 10 lessons x 30 minutes/week. The interaction and main effects for group and time on front crawl performances were not significant, indicating no differences were found between the two teaching methods. Also, the front crawl performances of the DCD swimmers did not change over the intervention period. The interaction and main effect for group on backstroke performances over the 10 weekly lessons were not significant. However, a significant main effect of time was found with backstroke changes found between Weeks 1 and 10 and Weeks 6 and 10. Sub-component analyses for horizontal body position, head position and breathing, and use of the lower limbs, revealed significant time effect improvements, but only between Weeks 1 and 10. Hence, both DCD groups improved their backstroke performances at the same rate across the 10 week intervention, despite being exposed to different instructional method

Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation

Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actual human behavior (e.g., multiple optimal actions), thereby limiting robot generalizability, applicability, and demonstration feasibility. To address this, this paper proposes designing imitation learning algorithms with a focus on utilizing human behavioral characteristics, thereby embodying principles for capturing and exploiting actual demonstrator behavioral characteristics. This paper presents the first imitation learning framework, Bayesian Disturbance Injection (BDI), that typifies human behavioral characteristics by incorporating model flexibility, robustification, and risk sensitivity. Bayesian inference is used to learn flexible non-parametric multi-action policies, while simultaneously robustifying policies by injecting risk-sensitive disturbances to induce human recovery action and ensuring demonstration feasibility. Our method is evaluated through risk-sensitive simulations and real-robot experiments (e.g., table-sweep task, shaft-reach task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate the improved characterisation of behavior. Results show significant improvement in task performance, through improved flexibility, robustness as well as demonstration feasibility.Comment: 69 pages, 9 figures, accepted by Elsevier Neural Networks - Journa

One-shot Empirical Privacy Estimation for Federated Learning

Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight. However, existing privacy auditing techniques usually make strong assumptions on the adversary (e.g., knowledge of intermediate model iterates or the training data distribution), are tailored to specific tasks and model architectures, and require retraining the model many times (typically on the order of thousands). These shortcomings make deploying such techniques at scale difficult in practice, especially in federated settings where model training can take days or weeks. In this work, we present a novel "one-shot" approach that can systematically address these challenges, allowing efficient auditing or estimation of the privacy loss of a model during the same, single training run used to fit model parameters, and without requiring any a priori knowledge about the model architecture or task. We show that our method provides provably correct estimates for privacy loss under the Gaussian mechanism, and we demonstrate its performance on a well-established FL benchmark dataset under several adversarial models

Unleashing the Power of Randomization in Auditing Differentially Private ML

We present a rigorous methodology for auditing differentially private machine learning algorithms by adding multiple carefully designed examples called canaries. We take a first principles approach based on three key components. First, we introduce Lifted Differential Privacy (LiDP) that expands the definition of differential privacy to handle randomized datasets. This gives us the freedom to design randomized canaries. Second, we audit LiDP by trying to distinguish between the model trained with $K$ canaries versus $K - 1$ canaries in the dataset, leaving one canary out. By drawing the canaries i.i.d., LiDP can leverage the symmetry in the design and reuse each privately trained model to run multiple statistical tests, one for each canary. Third, we introduce novel confidence intervals that take advantage of the multiple test statistics by adapting to the empirical higher-order correlations. Together, this new recipe demonstrates significant improvements in sample complexity, both theoretically and empirically, using synthetic and real data. Further, recent advances in designing stronger canaries can be readily incorporated into the new framework

The Student Movement Volume 107 Issue 23: So Long, Farewell: Students Step Into the Future

HUMANS Advice to Younger Selves, Interviewed by: Anna Pak Finals Stress Management, Gloria Oh Farewell to Pastor Dwight Nelson, Student Movement Staff What does AANHPI Heritage Month Mean?, Grace No ARTS & ENTERTAINMENT Currently: Reflecting on the Original Script, Solana Campbell In Memory of K.V. Rathnam, Ysabelle Fernando The Deal Premiers at Sonscreen Film Festival, Solana Campbell Through Their Eyes: AANHPI Expression, Amelia Stefanescu NEWS A Night at AU: SASA Cultural Night, Solana Campbell Interview with Professor Daniel Weber: Envision Magazine, Interviewed By: Brendan Oh Interview with Gloria Oh: Transforming an Idea into Reality, Interviewed By: Brendan Oh IDEAS All at Once: How AANHPI Media Representation Took Over 2023, Bella Hamann Raise A Glass to Freedom?, Terika Williams The Gem Off the Back of a Lorry, Gabi Francisco PULSE Goodbye and Welcome: Letters to the Incoming and Outgoing Presidents, The Andrews University Student Association Senate Honduras Mission, Interviewed By: Abraham Bravo Last Words for the School Year, Elizabeth Dovich In Summer: Professor Olaf Presents You the Ultimate Way to Spend Summer, Gloria Oh LAST WORD Take it from Me Part II, The Student Movement Staffhttps://digitalcommons.andrews.edu/sm-107/1022/thumbnail.jp

Learning to Generate Image Embeddings with User-level Differential Privacy

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of $\epsilon<2$ while controlling the utility drop within 5%, when millions of users can participate in training

The Student Movement Volume 107 Issue 12: Revisiting The Dream : Students Celebrate MLK Day

HUMANS Andrews Gaming Club, Interviewed by: Grace No Meet Gio Lee, Interviewed by: Nora Martin New Year, Happier Me, Gloria Oh ARTS & ENTERTAINMENT Art @ AU: Harrigan\u27s Gallery, Ysabelle Fernando Currently: The Way of Water, Solana Campbell Ode to 2022, Amelia Stefanescu Where Do I Find God - Part I, Anonymous NEWS Honoring Martin Luther King Jr.\u27s Legacy, Brendan Oh Is America Safe?: First Grader Shoots Teacher, Julia Randall A House Divided: Current Issues Within the School of Architecture and Interior Design, Student Movement Editorial Staff IDEAS Harry & Meghan: Unpacking Royal Pains, Gabriela Francisco A New Space for Creativity and Reaching Across Disciplinary Boundaries: The Inspiration Center, Peter Lyons, Anthony Bosman, Martin Hanna, Ryan Hayes, and Karin Thompson PULSE Our Food: Can They Cook It?, Melissa Moore Should We Have Bible Classes in the Core Curriculum?, Wambui Karanja What Comes First is a Question, Part II, Desmond H. Murray LAST WORD College in the Rearview Mirror, Scott Moncrieffhttps://digitalcommons.andrews.edu/sm-107/1011/thumbnail.jp

The Student Movement Volume 107 Issue 20: Andrews Students Spring Back from Break: Six More Weeks to Go

HUMANS Meeting Dr. McCree, Interviewed by: Grace No Interview with Dr. Luxton: Saying Goodbye, Interviewed by: Grace No Spring Break Renovations & Relaxation, Caryn Cruz ARTS & ENTERTAINMENT Currently: The Last of Us, Solana Campbell Spring Break Spotlight, Amelia Stefanescu What Happened to Wang in the Costco Bathroom?, Nora Martin NEWS Experiences Living in Lamson Hall, Abigail Kim Time is Ticking for TikTok, Brendan Oh WEAAU x CFE Service Sabbath, Terika Williams IDEAS Inequality Drags on in Tennessee, Alexander J. Hess On Value: True Crime and the Search for Meaning, Nora Martin When Winds Change: The Legacy of President Luxton, Bella Hamann PULSE A Trip to the Museo , Chris Ngugi AUSA Senates Holds Bon Appétit Forum, Neesa Richards Speaking Up With Women Press Release, Nicholas C. Gunn LAST WORD A Week of Rest and Relaxation ... Almost, Grace Nohttps://digitalcommons.andrews.edu/sm-107/1019/thumbnail.jp