When faced with a novel scenario, it can be hard to succeed on the first attempt. In these challenging situations, it is important to know how to retry quickly and meaningfully. Retrying behavior can emerge naturally in robots trained on diverse data, but such robot policies will typically only exhibit undirected retrying behavior and may not terminate a suboptimal approach before an unrecoverable mistake. We can improve these robot policies by instilling an explicit ability to try, evaluate, and retry a diverse range of strategies. We introduce Bellman-Guided Retrials, an algorithm that works on top of a base robot policy by monitoring the robot's progress, detecting when a change of plan is needed, and adapting the executed strategy until the robot succeeds. We start with a base policy trained on expert demonstrations of a variety of scenarios. Then, using the same expert demonstrations, we train a value function to estimate task completion. During test time, we use the value function to compare our expected rate of progress to our achieved rate of progress. If our current strategy fails to make progress at a reasonable rate, we recover the robot and sample a new strategy from the base policy while skewing it away from behaviors that have recently failed. We evaluate our method on simulated and real-world environments that contain a diverse suite of scenarios. We find that Bellman-Guided Retrials increases the average absolute success rates of base policies by more than 20% in simulation and 50% in real-world experiments, demonstrating a promising framework for instilling existing trained policies with explicit trial and error capabilities. For evaluation videos and other documentation, go to https://sites.google.com/view/to-err-robotic/home
Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach
School of Computing（计算学院）；Queen’s University（皇后大学）；Hotchkiss Brain Institute（霍克奇斯脑研究所）；University of Calgary（卡尔加里大学）；Centre for Neuroscience Studies（神经科学研究中心）；Providence Care Hospital（普罗维登斯护理医院）
Tailored robotic training improves hand function and proprioceptive processing in stroke survivors with proprioceptive deficits: A randomized controlled trial
University of New Mexico（新墨西哥大学）；UC Irvine（加州大学欧文分校）；National Center for Adaptive Neurotechnologies, Stratton VA Medical Center（斯特拉顿退伍军人医疗中心国家自适应神经技术中心）；Albany State University of New York（纽约州立大学奥尔巴尼分校）；University of Idaho（爱达荷大学）；UCLA（加州大学洛杉矶分校）；California Rehabilitation Institute（加州康复研究所）
TeleHairing: A Teleoperation Baseline for Robotic Haircutting
University of Oulu（奥卢大学）；Shaoyang University（邵阳学院）；Jishou University（吉首大学）；University of Exeter（埃克塞特大学）；University of Naples Federico II（那不勒斯腓特烈二世大学）；VTT Technical Research Centre of Finland（芬兰国家技术研究中心）
Robotic Task Success Evaluation Under Multi-modal Non-Parametric Object Pose Uncertainty
University of Southern Denmark（南丹麦大学）；SDU Robotics（SDU机器人学（注：SDU为南丹麦大学缩写））；Mærsk Mc-Kinney Møller Institute (MMMI)（马士基麦金姆穆勒研究所）；Faculty of Engineering（工程学院）；Danish Institute for Advanced Studies (DIAS)（丹麦高级研究院）
Learning Control as Enabling Layer for Embodied Intelligence Research explored with Soft Robotic Swimming in diverse Flow Speeds
Engineering Sciences Department, Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术实验室工程科学系）；Institute for NeuroInformatics, University of Zurich（苏黎世大学神经信息研究所）；Department of Electronic Engineering, University of Rome Tor Vergata（罗马大学Tor Vergata电子工程系）
Dual-Mode Synchronization Predictive Control of Robotic Manipulator
School of Mechanical and Electrical Engineering, Guangzhou University（广州大学机械与电气工程学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
