2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)最新文献

Thermal-Inertial Odometry for Autonomous Flight Throughout the Night 用于夜间自主飞行的热惯性里程计

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968238

J. Delaune, Robert A. Hewitt, Laura Lytle, C. Sorice, Rohan Thakker, L. Matthies

引用次数: 20

Miniaturization of MR Safe Pneumatic Rotational Stepper Motors MR安全气动旋转步进电机的小型化

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968262

V. Groenhuis, F. Siepel, S. Stramigioli

引用次数: 5

Haptic Perception of Liquids Enclosed in Containers 容器内液体的触觉感知

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968528

Carolyn Matl, R. Matthew, R. Bajcsy

引用次数: 11

A Penetration Metric for Deforming Tetrahedra using Object Norm 基于对象范数的四面体变形穿透度量

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967563

Jisu Kim, Young J. Kim

{"title":"A Penetration Metric for Deforming Tetrahedra using Object Norm","authors":"Jisu Kim, Young J. Kim","doi":"10.1109/IROS40897.2019.8967563","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8967563","url":null,"abstract":"In this paper, we propose a novel penetration metric, called deformable penetration depth PDd, to define a measure of inter-penetration between two linearly deforming tetrahedra using the object norm [1]. First of all, we show that a distance metric for a tetrahedron deforming between two configurations can be found in closed form based on object norm. Then, we show that the PDd between an intersecting pair of static and deforming tetrahedra can be found by solving a quadratic programming (QP) problem in terms of the distance metric with non-penetration constraints. We also show that the PDd between two, intersected, deforming tetrahedra can be found by solving a similar QP problem under some assumption on penetrating directions, and it can be also accelerated by an order of magnitude using pre-calculated penetration direction. We have implemented our algorithm on a standard PC platform using an off-the-shelf QP optimizer, and experimentally show that both the static/deformable and deformable/deformable tetrahedra cases can be solvable in from a few to tens of milliseconds. Finally, we demonstrate that our penetration metric is three-times smaller (or tighter) than the classical, rigid penetration depth metric in our experiments.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124802616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning to Sequence Multiple Tasks with Competing Constraints 学习排序具有竞争约束的多个任务

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968496

Anqing Duan, R. Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, L. Rosasco, D. Pucci

引用次数: 6

Avoiding Obstacles during Push Recovery Using Real-Time Vision Feedback 使用实时视觉反馈在推恢复过程中避免障碍物

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968009

Hyobin Jeong, Joon-ha Kim, Okkee Sim, Jun-Ho Oh

引用次数: 6

Dempster Shafer Grid-based Hybrid Fusion of Virtual Lanes for Autonomous Driving 基于Dempster Shafer网格的自动驾驶虚拟车道混合融合

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967610

Ferit Üzer, R. Benmokhtar, Salma Moujtahid, Xavier Perrotton

引用次数: 1

A-EXP4: Online Social Policy Learning for Adaptive Robot-Pedestrian Interaction A-EXP4:自适应机器人-行人互动的在线社会政策学习

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967737

Pengju Jin, Eshed Ohn-Bar, Kris M. Kitani, C. Asakawa

{"title":"A-EXP4: Online Social Policy Learning for Adaptive Robot-Pedestrian Interaction","authors":"Pengju Jin, Eshed Ohn-Bar, Kris M. Kitani, C. Asakawa","doi":"10.1109/IROS40897.2019.8967737","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8967737","url":null,"abstract":"We study self-supervised adaptation of a robot's policy for social interaction, i.e., a policy for active communication with surrounding pedestrians through audio or visual signals. Inspired by the observation that humans continually adapt their behavior when interacting under varying social context, we propose Adaptive EXP4 (A-EXP4), a novel online learning algorithm for adapting the robot-pedestrian interaction policy. To address limitations of bandit algorithms in adaptation to unseen and highly dynamic scenarios, we employ a mixture model over the policy parameter space. Specifically, a Dirichlet Process Gaussian Mixture Model (DPMM) is used to cluster the parameters of sampled policies and maintain a mixture model over the clusters, hence effectively discovering policies that are suitable to the current environmental context in an unsupervised manner. Our simulated and real-world experiments demonstrate the feasibility of A-EXP4 in accommodating interaction with different types of pedestrians while jointly minimizing social disruption through the adaptation process. While the A-EXP4 formulation is kept general for application in a variety of domains requiring continual adaptation of a robot's policy, we specifically evaluate the performance of our algorithm using a suitcase-inspired assistive robotic platform. In this concrete assistive scenario, the algorithm observes how audio signals produced by the navigational system affect the behavior of pedestrians and adapts accordingly. Consequently, we find A-EXP4 to effectively adapt the interaction policy for gently clearing a navigation path in crowded settings, resulting in significant reduction in empirical regret compared to the EXP4 baseline.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126180107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Towards Reversible Dynamic Movement Primitives 走向可逆动态运动基元

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968270

Iñigo Iturrate, Christoffer Sloth, Aljaz Kramberger, H. G. Petersen, E. H. Østergaard, T. Savarimuthu

{"title":"Towards Reversible Dynamic Movement Primitives","authors":"Iñigo Iturrate, Christoffer Sloth, Aljaz Kramberger, H. G. Petersen, E. H. Østergaard, T. Savarimuthu","doi":"10.1109/IROS40897.2019.8968270","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8968270","url":null,"abstract":"In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system’s temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123397008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Comparison of Deep Reinforcement Learning Policies to Formal Methods for Moving Obstacle Avoidance 移动避障的深度强化学习策略与形式化方法的比较

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967945

Arpit Garg, H. Chiang, S. Sugaya, Aleksandra Faust, Lydia Tapia

{"title":"Comparison of Deep Reinforcement Learning Policies to Formal Methods for Moving Obstacle Avoidance","authors":"Arpit Garg, H. Chiang, S. Sugaya, Aleksandra Faust, Lydia Tapia","doi":"10.1109/IROS40897.2019.8967945","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8967945","url":null,"abstract":"Deep Reinforcement Learning (RL) has recently emerged as a solution for moving obstacle avoidance. Deep RL learns to simultaneously predict obstacle motions and corresponding avoidance actions directly from robot sensors, even for obstacles with different dynamics models. However, deep RL methods typically cannot guarantee policy convergences, i.e., cannot provide probabilistic collision avoidance guarantees. In contrast, stochastic reachability (SR), a computationally expensive formal method that employs a known obstacle dynamics model, identifies the optimal avoidance policy and provides strict convergence guarantees. The availability of the optimal solution for versions of the moving obstacle problem provides a baseline to compare trained deep RL policies. In this paper, we compare the expected cumulative reward and actions of these policies to SR, and find the following. 1) The state-value function approximates the optimal collision probability well, thus explaining the high empirical performance. 2) RL policies deviate from the optimal significantly thus negatively impacting collision avoidance in some cases. 3) Evidence suggests that the deviation is caused, at least partially, by the actor net failing to approximate the action corresponding to the highest state-action value.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125252606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8