2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)最新文献_第3页

Learning to Grasp by Extending the Peri-Personal Space Graph 通过扩展周边个人空间图来学习把握

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8593938

J. Juett, B. Kuipers

{"title":"Learning to Grasp by Extending the Peri-Personal Space Graph","authors":"J. Juett, B. Kuipers","doi":"10.1109/IROS.2018.8593938","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593938","url":null,"abstract":"We present a robot model of early reach and grasp learning, inspired by infant learning without prior knowledge of the geometry, kinematics, or dynamics of the arm. Human infants at reach onset are capable of using a sequence of jerky submotions to bring the hand to the position of a nearby object. A robotic learning agent can produce qualitatively similar behavior by using a graph representation to encode a set of safe, potentially useful arm states and feasible moves between them. These observations show that the Peri-Personal Space (PPS) Graph model is sufficient for early reaching and suggest that infants may use analogous models during this phase. In this paper, we show that the PPS Graph, with a simulated Palmar reflex (a reflex in infants that closes the fingers when the palm is touched), allows accidental grasps to occur during continued reaching practice. Given these occasional events, the agent can bootstrap to a simple deliberate grasp action. In particular, the agent must learn three new necessary conditions for a grasp: the hand should be open as the grasp begins, the final motion of the hand should be led by the gripper opening so that it reaches the target first, and the wrist must be oriented such that the gripper fingers may close around the target object, often requiring the opening to be perpendicular to the object's major axis. Combined with the existing capability to reach and interact with target objects, knowledge of these conditions allows the agent to learn increasingly reliable purposeful grasps. The first two conditions are addressed in this paper, and allow 45% of grasps to succeed. This work contributes toward the larger goal of foundational robot learning after the model of infant learning, with minimal prior knowledge of its own anatomy or its environment. The ability to grasp will allow the agent to control the motion and position of objects, providing a richer representation for its environment and new experiences to learn from.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"12 1","pages":"8695-8700"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80162819","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: 4

3D Deep Object Recognition and Semantic Understanding for Visually-Guided Robotic Service 面向视觉引导机器人服务的三维深度对象识别和语义理解

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8593985

Sukhan Lee, A. Naguib, N. Islam

{"title":"3D Deep Object Recognition and Semantic Understanding for Visually-Guided Robotic Service","authors":"Sukhan Lee, A. Naguib, N. Islam","doi":"10.1109/IROS.2018.8593985","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593985","url":null,"abstract":"For the success of visually-guided robotic errand service, it is critical to ensure dependability under various ill-conditioned visual environments. To this end, we have developed Adaptive Bayesian Recognition Framework in which in-situ selection of multiple sets of optimal features or evidences as well as proactive collection of sufficient evidences are proposed to implement the principle of dependability. The framework has shown excellent performance with a limited number of objects in a scene. However, there arises a need to extend the framework for handling a larger number of objects without performance degradation, while avoiding difficulty in feature engineering. To this end, a novel deep learning architecture, referred to here as FER-CNN, is introduced and integrated into the Adaptive Bayesian Recognition Framework. FER-CNN has capability of not only extracting but also reconstructing a hierarchy of features with the layer-wise independent feedback connections that can be trained. Reconstructed features representing parts of 3D objects then allow them to be semantically linked to ontology for exploring object categories and properties. Experiments are conducted in a home environment with real 3D daily-life objects as well as with the standard ModelNet dataset. In particular, it is shown that FER-CNN allows the number of objects and their categories to be extended by 10 and 5 times, respectively, while registering the recognition rate for ModelNet10 and ModelNet40 by 97% and 89.5%, respectively.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"2 1","pages":"903-910"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79439146","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

A Framework for Dexterous Manipulation 灵巧操作的框架

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8594497

L. Ku, J. Rogers, Philip Strawser, Julia M. Badger, E. Learned-Miller, R. Grupen

引用次数: 0

Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning 基于深度强化学习的多旋翼航空机器人激光响应导航

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8593706

Carlos Sampedro, Hriday Bavle, Alejandro Rodriguez-Ramos, P. D. L. Puente, P. Campoy

引用次数: 32

Smooth Point-to-Point Trajectory Planning in $SE$ (3)with Self-Collision and Joint Constraints Avoidance 具有自碰撞和关节约束回避的$SE$(3)光滑点对点轨迹规划

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8594339

R. Grassmann, Lars Johannsmeier, S. Haddadin

引用次数: 6

Contingent Contact-Based Motion Planning 基于偶然接触的运动规划

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8594365

Elod Páll, Arne Sieverling, O. Brock

引用次数: 20

Design of SUPERball v2, a Compliant Tensegrity Robot for Absorbing Large Impacts 抗冲击柔性张拉整体机器人SUPERball v2的设计

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8594374

Massimo Vespignani, Jeffrey M. Friesen, Vytas SunSpiral, J. Bruce

引用次数: 49

An Efficient and Time-Optimal Trajectory Generation Approach for Waypoints Under Kinematic Constraints and Error Bounds 一种基于运动约束和误差边界的航路点轨迹生成方法

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8593577

Jianjie Lin, N. Somani, Biao Hu, Markus Rickert, A. Knoll

{"title":"An Efficient and Time-Optimal Trajectory Generation Approach for Waypoints Under Kinematic Constraints and Error Bounds","authors":"Jianjie Lin, N. Somani, Biao Hu, Markus Rickert, A. Knoll","doi":"10.1109/IROS.2018.8593577","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593577","url":null,"abstract":"This paper presents an approach to generate the time-optimal trajectory for a robot manipulator under certain kinematic constraints such as joint position, velocity, acceleration, and jerk limits. This problem of generating a trajectory that takes the minimum time to pass through specified waypoints is formulated as a nonlinear constraint optimization problem. Unlike prior approaches that model the motion of consecutive waypoints as a Cubic Spline, we model this motion with a seven-segment acceleration profile, as this trajectory results in a shorter overall motion time while staying within the bounds of the robot manipulator's constraints. The optimization bottleneck lies in the complexity that increases exponentially with the number of waypoints. To make the optimization scale well with the number of waypoints, we propose an approach that has linear complexity. This approach first divides all waypoints to consecutive batches, each with an overlap of two waypoints. The overlapping waypoints then act as a bridge to concatenate the optimization results of two consecutive batches. The whole trajectory is effectively optimized by successively optimizing every batch. We conduct experiments on practical scenarios and trajectories generated by motion planners to evaluate the effectiveness of our proposed approach over existing state-of-the-art approaches.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"56 1","pages":"5869-5876"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85046069","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: 21

Neurorobotic Approach to Study Huntington Disease Based on a Mouse Neuromusculoskeletal Model 基于小鼠神经肌肉骨骼模型的神经机器人方法研究亨廷顿病

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8594491

S. Oota, Y. Okamura-Oho, Ko Ayusawa, Y. Ikegami, A. Murai, E. Yoshida, Yoshihiko Nakamura

{"title":"Neurorobotic Approach to Study Huntington Disease Based on a Mouse Neuromusculoskeletal Model","authors":"S. Oota, Y. Okamura-Oho, Ko Ayusawa, Y. Ikegami, A. Murai, E. Yoshida, Yoshihiko Nakamura","doi":"10.1109/IROS.2018.8594491","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594491","url":null,"abstract":"Motor functions of the biological system has been forged through 4 billion years evolution. From a neurorobotics view, it is important not only to know how well it works, but also how it fails. To quantitatively describe early onset symptoms of a neurodegenerative disease, we analyzed phenotypes of genetically engineered Huntington disease (HD) model mice, which reveal progressive impaired motor functions. We devised a simple yet sensitive paradigm called the crystalized motion profile (CMP), by which we successfully detected subtle difference between normal and abnormal mice in terms of whole-body level motor coordination. Our long-term objective is to remodel human mind and body to regain impaired motor and cognitive functions with ageing. To do so, we are developing a soft neurorobotic suit that provides integrated cognitive and physical interventions to users. Our analysis on the HD model mice is important as the first step to bridge between molecular mechanisms (altered genetic code) and the macroscopic neuro-musculoskeletal model. With this, we can extrapolate from knowledge of non-human mammals to human to derive the remodeling.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"5 1","pages":"6720-6727"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85238087","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: 2

Continuum Manipulator with Redundant Backbones and Constrained Bending Curvature for Continuously Variable Stiffness 连续变刚度的冗余骨架约束弯曲连续机械臂

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2018-10-01 DOI: 10.1109/IROS.2018.8593437

Bin Zhao, Weihao Zhang, Zhaoyu Zhang, Xiangyang Zhu, Kai Xu

引用次数: 11