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Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
[article]
2016
arXiv
pre-print
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We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace. ...
To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. ...
The approach, based on Inverse Optimal Control (IOC) and Goal Set Iterative replanning allows us to find a cost function balancing different features that outperforms hand-tuning of the cost function in ...
arXiv:1606.02111v1
fatcat:adk7hwvfabekpf2dzf65jpnkeq
Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning
2015
2015 IEEE International Conference on Robotics and Automation (ICRA)
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To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. ...
While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing ...
Predicting Human Reaching Motion in Collaborative Tasks Using Inverse Optimal Control and Iterative Re-planning Abstract-To enable safe and efficient human-robot collaboration in shared workspaces, it ...
doi:10.1109/icra.2015.7139282
dblp:conf/icra/MainpriceHB15
fatcat:55c6e7shlfckbe7c42z7luoama
Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks
[article]
2021
arXiv
pre-print
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Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. ...
In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). ...
The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Philipp Kratzer. ...
arXiv:2104.08137v2
fatcat:fy2zvplcpvbgxf3ch46s2xga6q
Continuous Motion Planning for Service Robots with Multiresolution in Time
[chapter]
2015
Advances in Intelligent Systems and Computing
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Our approach is based on stochastic trajectory optimization for motion planning (STOMP) and designed to decrease the optimization time in order to enable frequent replanning. ...
In addition to employing temporal multiresolution, we speed up trajectory optimization by initializing replanning with the previous plan. ...
The
minimum number of iterations is set to 19 for this experiment. ...
doi:10.1007/978-3-319-08338-4_16
fatcat:ypfirlal6rai3pvwpuo4orwghi
An Efficient Online Trajectory Generation Method Based on Kinodynamic Path Search and Trajectory Optimization for Human-Robot Interaction Safety
2022
Entropy
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With the rapid development of robot perception and planning technology, robots are gradually getting rid of fixed fences and working closely with humans in shared workspaces. ...
The safety of human-robot coexistence has become critical. Traditional motion planning methods perform poorly in dynamic environments where obstacles motion is highly uncertain. ...
In these applications, robots act as human partners, with side-by-side or face-to-face working with humans in shared workspaces to complete specific tasks, and the safety issue of human-robot coexistence ...
doi:10.3390/e24050653
fatcat:gv3izev2kjbbxao4w442nsfup4
Human-Aware Robotic Assistant for Collaborative Assembly: Integrating Human Motion Prediction With Planning in Time
2018
IEEE Robotics and Automation Letters
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For instance, researchers have developed techniques utilizing inverse optimal control [19] and Bayesian classification [20] to generate motion predictors using labeled data of arm motion. ...
One such approach leverages the assumption that people move in an efficient manner while navigating an environment in order to model human motions using maximum entropy inverse optimal control [24] . ...
doi:10.1109/lra.2018.2812906
dblp:journals/ral/UnhelkarLTBMDS18
fatcat:z5jpofexgbfzrobiadxqdhxnvq
Introduction to the Special Issue on Movement Science for Humans and Humanoids
2016
IEEE Transactions on robotics
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in humanoid robotics, and human motor control. ...
His research interests include humanoid robot control and motion synthesis, physical human-robot interaction, character animation, and human motion simulation. Dr. ...
The paper "Goal Set Inverse Optimal Control and Iterative Replanning for Predicting Human Reaching Motions in Shared Workspaces," by Jim Mainprice, Rafi Hayne, and Dmitry Berenson, develops an approach ...
doi:10.1109/tro.2016.2593807
fatcat:nkz7uw3s5re3rg3qtnga7y3yjy
Workspace monitoring and planning for safe mobile manipulation
[article]
2020
arXiv
pre-print
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By integrating the monitoring, planning, and interaction control components, the task of grasping, placing and delivering objects to humans in a shared workspace is demonstrated. ...
In order to enable physical human-robot interaction where humans and (mobile) manipulators share their workspace and work together, robots have to be equipped with important capabilities to guarantee human ...
Acknowledgement The presented research has been supported by the European Commission's 7th Framework Programme as part of the project SAPHARI (Safe and Autonomous Physical Human-Aware Robot Interaction ...
arXiv:2006.01546v1
fatcat:idpwi23ujnhbtgozo26vvhl4gm
Map-Predictive Motion Planning in Unknown Environments
[article]
2019
arXiv
pre-print
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We present a unified method that combines map prediction and motion planning for safe, time-efficient autonomous navigation of unknown environments by dynamically-constrained robots. ...
Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. ...
States and controls share the same subscript convention. ...
arXiv:1910.08184v1
fatcat:d7sp36s3f5ab7itp6mp2savbx4
Multi 3D camera mapping for predictive and reflexive robot manipulator trajectory estimation
2016
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
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2016 pre-print arXiv:1610.03646v1 fatcat:hspdd3gnlzhpjimdxomz3cms5m
The main goal of our work is to make the operation more flexible in unstructured dynamic workspaces and not just avoid obstacles, but also adapt when performing collaborative tasks with humans in the near ...
We present a method combining 3D Camera based workspace mapping, and a predictive and reflexive robot manipulator trajectory estimation to allow more efficient and safer operation in dynamic workspaces ...
ACKNOWLEDGMENT This work is partially supported by The Research Council of Norway as a part of the Engineering Predictability with Embodied Cognition (EPEC) project, under grant agreement 240862 ...
doi:10.1109/ssci.2016.7850237
dblp:conf/ssci/MiseikisGET16
fatcat:ksoe7oth4jcjdhf6jrgb4z2xri
Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks
[article]
2020
arXiv
pre-print
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In order to alleviate this issue, we propose a prediction framework that decouples short-term prediction, linked to internal body dynamics, and long-term prediction, linked to the environment and task ...
In this work we investigate encoding short-term dynamics in a recurrent neural network, while we account for environmental constraints, such as obstacle avoidance, using gradient-based trajectory optimization ...
The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Philipp Kratzer. ...
arXiv:1910.01843v2
fatcat:ugb5sqsa2nbhxc7c4j7bsle6oi
CHOMP: Covariant Hamiltonian optimization for motion planning
2013
The international journal of robotics research
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In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. ...
CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. ...
Acknowledgments We thank Gil Jones and Willow Garage for their support, particularly in performing ROS experiments. ...
doi:10.1177/0278364913488805
fatcat:lelp5mb6xrbwpli6npvpotli74
Differentially constrained mobile robot motion planning in state lattices
2009
Journal of Field Robotics
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We compute a set of elementary motions that connects each discrete state value to a set of its reachable neighbors via feasible motions. Thus, this set of motions induces a connected search graph. ...
We ensure that all paths in the graph encode feasible motions via the imposition of continuity constraints on state variables at graph vertices and compliance of the graph edges with a differential equation ...
Control Sets with Shortest Edges Algorithm 1 is a simple inverse method for generating a control set, as introduced in Section 2.1. ...
doi:10.1002/rob.20285
fatcat:ro2rxetkpncnbgfwxqh3zez7ge
Human-aware motion reshaping using dynamical systems
2017
Pattern Recognition Letters
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In this work, we present a real-time approach for human-aware motion replanning using a two-level hierarchical architecture. ...
in case of unforeseen obstacles (including the human), and iii) to replan online the current task taking into account the human behavior. ...
Graduate School of Science and Engineering. ...
doi:10.1016/j.patrec.2017.04.014
fatcat:rmhry6ctz5d3vcexcjo4mn32me
High-DOF Robots in Dynamic Environments Using Incremental Trajectory Optimization
2014
International Journal of Humanoid Robotics
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We present a novel optimization-based motion planning algorithm for high degree-offreedom (DOF) robots in dynamic environments. ...
We compute collision-free and smooth paths using optimization-based planning and trajectory perturbation for each sub-problem. ...
Acknowledgments This research is supported in part by ARO Contracts W911NF-10-1-0506 and NSF awards 1000579, 1117127, 1305286. ...
doi:10.1142/s0219843614410011
fatcat:fi2jtbnnljeojfhdqbbokhl2xi
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