Robotics science and systems : online proceedings最新文献_第2页

Shared Autonomy via Hindsight Optimization. 通过后见之明优化共享自主权。

Robotics science and systems : online proceedings Pub Date : 2015-07-01 DOI: 10.15607/RSS.2015.XI.032

Shervin Javdani, Siddhartha S Srinivasa, J Andrew Bagnell

{"title":"Shared Autonomy via Hindsight Optimization.","authors":"Shervin Javdani, Siddhartha S Srinivasa, J Andrew Bagnell","doi":"10.15607/RSS.2015.XI.032","DOIUrl":"10.15607/RSS.2015.XI.032","url":null,"abstract":"In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve, and must both predict the user's intended goal, and assist in achieving that goal. We formulate the problem of shared autonomy as a Partially Observable Markov Decision Process with uncertainty over the user's goal. We utilize maximum entropy inverse optimal control to estimate a distribution over the user's goal based on the history of inputs. Ideally, the robot assists the user by solving for an action which minimizes the expected cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal action is intractable, we use hindsight optimization to approximate the solution. In a user study, we compare our method to a standard predict-then-blend approach. We find that our method enables users to accomplish tasks more quickly while utilizing less input. However, when asked to rate each system, users were mixed in their assessment, citing a tradeoff between maintaining control authority and accomplishing tasks quickly.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"2015 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329599/pdf/nihms801800.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36858416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic Temporal Logic for Motion Planning with Resource Threshold Constraints 资源阈值约束下运动规划的概率时间逻辑

Robotics science and systems : online proceedings Pub Date : 2012-07-09 DOI: 10.15607/RSS.2012.VIII.058

C. Yoo, R. Fitch, S. Sukkarieh

引用次数: 18

Robust Loop Closing Over Time 鲁棒循环随时间闭合

Robotics science and systems : online proceedings Pub Date : 2012-07-09 DOI: 10.15607/RSS.2012.VIII.030

Y. Latif, C. C. Lerma, José Neira

引用次数: 72

Experiments with Balancing on Irregular Terrains using the Dreamer Mobile Humanoid Robot 梦想者移动人形机器人在不规则地形上的平衡实验

Robotics science and systems : online proceedings Pub Date : 2012-07-09 DOI: 10.15607/RSS.2012.VIII.050

L. Sentis, Josh Petersen, Roland Philippsen

引用次数: 13

Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries 时延不确定性下的实用路径规划:随机最短路径查询

Robotics science and systems : online proceedings Pub Date : 2012-07-09 DOI: 10.15607/RSS.2012.VIII.032

Sejoon Lim, Christian Sommer, E. Nikolova, D. Rus

{"title":"Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries","authors":"Sejoon Lim, Christian Sommer, E. Nikolova, D. Rus","doi":"10.15607/RSS.2012.VIII.032","DOIUrl":"https://doi.org/10.15607/RSS.2012.VIII.032","url":null,"abstract":"We describe an algorithm for stochastic path planning and applications to route planning in the presence of traffic delays. We improve on the prior state of the art by designing, analyzing, implementing, and evaluating data structures that answer approximate stochastic shortest-path queries. For example, our data structures can be used to efficiently compute paths that maximize the probability of arriving at a destination before a given time deadline. Our main theoretical result is an algorithm that, given a directed planar network with edge lengths characterized by expected travel time and variance, pre-computes a data structure in quasi-linear time such that approximate stochastic shortestpath queries can be answered in poly-logarithmic time (actual worst-case bounds depend on the probabilistic model). Our main experimental results are two-fold: (i) we provide methods to extract travel-time distributions from a large set of heterogenous GPS traces and we build a stochastic model of an entire city, and (ii) we adapt our algorithms to work for realworld road networks, we provide an efficient implementation, and we evaluate the performance of our method for the model of the aforementioned city.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"52 1","pages":"249-256"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91397301","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: 39

Motion Planning Under Uncertainty In Highly Deformable Environments. 高变形环境下不确定运动规划。

Robotics science and systems : online proceedings Pub Date : 2011-06-01 DOI: 10.15607/rss.2011.vii.033

Sachin Patil, Jur van den, Berg Ron Alterovitz

{"title":"Motion Planning Under Uncertainty In Highly Deformable Environments.","authors":"Sachin Patil, Jur van den, Berg Ron Alterovitz","doi":"10.15607/rss.2011.vii.033","DOIUrl":"https://doi.org/10.15607/rss.2011.vii.033","url":null,"abstract":"Many tasks in robot-assisted surgery, food handling, manufacturing, and other applications require planning and controlling the motions of manipulators or other devices that must interact with highly deformable objects. We present a unified approach for motion planning under uncertainty in deformable environments that maximizes probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. Unlike prior planners that assume deterministic deformations or treat deformations as a type of small perturbation, our method explicitly considers the uncertainty in large, time-dependent deformations. Our method requires a simulator of deformable objects but places no significant restrictions on the simulator used. We use a sampling-based motion planner in conjunction with the simulator to generate a set of candidate plans based on expected deformations. Our method then uses the simulator and optimal control to numerically estimate time-dependent state distributions based on uncertain parameters (e.g. deformable material properties or actuation errors). We then select the plan with the highest estimated probability of successfully avoiding obstacles and reaching the goal region. Using FEM-based simulation of deformable tissues, we demonstrate the ability of our method to generate high quality plans in two medical-inspired scenarios: (1) guiding bevel-tip steerable needles through slices of deformable tissue around obstacles for minimally invasive biopsies and drug-delivery, and (2) manipulating planar tissues to align interior points at desired coordinates for precision treatment.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096573/pdf/nihms-346279.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32507537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52

Feedback Control for Steering Needles Through 3D Deformable Tissue Using Helical Paths. 螺旋路径导向针通过三维可变形组织的反馈控制。

Robotics science and systems : online proceedings Pub Date : 2009-06-28 DOI: 10.15607/rss.2009.v.037

Kris Hauser, Ron Alterovitz, Nuttapong Chentanez, Allison Okamura, Ken Goldberg

{"title":"Feedback Control for Steering Needles Through 3D Deformable Tissue Using Helical Paths.","authors":"Kris Hauser, Ron Alterovitz, Nuttapong Chentanez, Allison Okamura, Ken Goldberg","doi":"10.15607/rss.2009.v.037","DOIUrl":"https://doi.org/10.15607/rss.2009.v.037","url":null,"abstract":"Bevel-tip steerable needles are a promising new technology for improving accuracy and accessibility in minimally invasive medical procedures. As yet, 3D needle steering has not been demonstrated in the presence of tissue deformation and uncertainty, despite the application of progressively more sophisticated planning algorithms. This paper presents a feedback controller that steers a needle along 3D helical paths, and varies the helix radius to correct for perturbations. It achieves high accuracy for targets sufficiently far from the needle insertion point; this is counterintuitive because the system is highly under-actuated and not locally controllable. The controller uses a model predictive control framework that chooses a needle twist rate such that the predicted helical trajectory minimizes the distance to the target. Fast branch and bound techniques enable execution at kilohertz rates on a 2GHz PC. We evaluate the controller under a variety of simulated perturbations, including imaging noise, needle deflections, and curvature estimation errors. We also test the controller in a 3D finite element simulator that incorporates deformation in the tissue as well as the needle. In deformable tissue examples, the controller reduced targeting error by up to 88% compared to open-loop execution.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"V ","pages":"37"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3004482/pdf/nihms191732.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29553373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 75

Fishbone Model for Belt Object Deformation 带物体变形的鱼骨模型

Robotics science and systems : online proceedings Pub Date : 2007-06-27 DOI: 10.15607/RSS.2007.III.012

H. Wakamatsu, E. Arai, S. Hirai

引用次数: 9

Efficient Motion Planning Based on Disassembly 基于拆卸的高效运动规划

Robotics science and systems : online proceedings Pub Date : 2005-06-08 DOI: 10.15607/RSS.2005.I.014

Yuandong Yang, O. Brock

{"title":"Efficient Motion Planning Based on Disassembly","authors":"Yuandong Yang, O. Brock","doi":"10.15607/RSS.2005.I.014","DOIUrl":"https://doi.org/10.15607/RSS.2005.I.014","url":null,"abstract":"Disassembly-based motion planning (DBMP) is a novel and efficient single-query, sampling-based motion planning approach for free-flying robots. Disassembly-based motion planning uses workspace information to determine the workspace volume of a potential solution path and uses this information to exclude large portions of configuration space from exploration. It also identifies the most constrained placements of the robot along the potential solution path. These placements are referred to as assemblies because they are highly constrained by the environment, much like parts in an assembly are constrained. The constraints limit the possible motions of the robot and thus can be exploited to further limit configuration space exploration. The use of these two sources of workspace information permits the solution of many practical problems with very limited configuration space exploration. This reduction in configuration space exploration results in performance improvements of several orders of magnitude, compared to state-of-the-art motion planning methods. For non-free-flying robots, disassembly-based motion planning performs at least as well as the sampling-based motion planning method it is based on.","PeriodicalId":87357,"journal":{"name":"Robotics science and systems : online proceedings","volume":"8 1","pages":"97-104"},"PeriodicalIF":0.0,"publicationDate":"2005-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74667556","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: 34

Vision-based Distributed Coordination and Flocking of Multi-agent Systems 基于视觉的多智能体系统分布式协调与群集

Robotics science and systems : online proceedings Pub Date : 2005-06-08 DOI: 10.15607/RSS.2005.I.006

N. Moshtagh, A. Jadbabaie, Kostas Daniilidis

引用次数: 26