Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models. This paper analyses the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in its low dimensional subspace by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviours by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.
Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots
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）
Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
Beijing Institute of Technology（北京理工大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Loop Area Institute（深圳河套学院）；Hunan University（湖南大学）；Xi’an Jiaotong University（西安交通大学）；Renmin University of China（中国人民大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
The RATTLE Motion Planning Algorithm for Robust Online Parametric Model Improvement with On-Orbit Validation
SGT Inc.（SGT公司）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Institute for Systems and Robotics（系统与机器人研究所）；Instituto Superior Técnico（高等技术学院）；Massachusetts Institute of Technology（麻省理工学院）
Online Information-Aware Motion Planning with Inertial Parameter Learning for Robotic Free-Flyers
SGT Inc.（SGT公司）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Institute for Systems and Robotics（系统与机器人研究所）；Instituto Superior Técnico（里斯本高等理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Residual Deep Reinforcement Learning-Based Computed Torque Control for a Cable-Driven Lower-Limb Rehabilitation Robot under Disturbances and Parametric Uncertainties
Endowing Robots with Longer-term Autonomy by Recovering from External Disturbances in Manipulation through Grounded Anomaly Classification and Recovery Policies
