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Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning

Jim Mainprice, Rafi Hayne, Dmitry Berenson
2015 2015 IEEE International Conference on Robotics and Automation (ICRA)  
Finally, we predict a human's motion for a given task by iteratively re-planning a trajectory for a 23 DoFs human kinematic model using the STOMP algorithm with the learned cost function in the presence  ...  We then use Inverse Optimal Control to learn a cost function from these trajectories.  ...  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

Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces [article]

Jim Mainprice and Rafi Hayne and Dmitry Berenson
2016 arXiv   pre-print
Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function.  ...  We then use Inverse Optimal Control to learn a cost function from these trajectories.  ...  We use goal set trajectory optimization in both IOC and Iterative Re-Planning.  ... 
arXiv:1606.02111v1 fatcat:adk7hwvfabekpf2dzf65jpnkeq

Collaborative Behavior Models for Optimized Human-Robot Teamwork [article]

Adam Fishman, Chris Paxton, Wei Yang, Dieter Fox, Byron Boots, Nathan Ratliff
2020 arXiv   pre-print
In this work, we describe a novel Model Predictive Control (MPC)-based framework for finding optimal trajectories in a collaborative, multi-agent setting, in which we simultaneously plan for the robot  ...  This cyclical game of predicting a human's future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques.  ...  [11] use probabilistic motion primitives to model both humans and robots in a variety of collaborative tasks, including handover. Zhou et al.  ... 
arXiv:1910.04339v2 fatcat:gg4qlehnwfc2nd2btsbzr5dtzu

A Hybrid Framework for Understanding and Predicting Human Reaching Motions

Ozgur S. Oguz, Zhehua Zhou, Dirk Wollherr
2018 Frontiers in Robotics and AI  
Robots collaborating naturally with a human partner in a confined workspace need to understand and predict human motions.  ...  The proposed framework affords a descriptive and a generative model of human reaching motions which can be effectively utilized online for human-in-the-loop robot control and task execution.  ...  AUTHOR CONTRIBUTIONS OO formulated and initiated the research. ZZ finalized the research and conducted the experiments. OO, ZZ, and DW wrote the paper.  ... 
doi:10.3389/frobt.2018.00027 pmid:33500914 pmcid:PMC7806050 fatcat:ovlxyuwdebbkjc4bjgy6m7xr4a

Anticipatory Human-Robot Collaboration via Multi-Objective Trajectory Optimization [article]

Abhinav Jain, Daphne Chen, Dhruva Bansal, David Kent, Harish Ravichandar, Sonia Chernova
2020 arXiv   pre-print
We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in human-robot collaborative tasks.  ...  To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction models to anticipate the human's motion and adapt the robot's joint trajectory accordingly  ...  Fishman et al. address the problem of coordinated human-robot collaboration, specifically in a handover task [5] , using a joint optimal control model to simultaneously plan the robot's behavior and predict  ... 
arXiv:2006.03614v1 fatcat:t4vgcfsbhzc73b7qovdiztvfge

Human-Like Arm Motion Generation: A Review

Gianpaolo Gulletta, Wolfram Erlhagen, Estela Bicho
2020 Robotics  
For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction.  ...  In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning.  ...  For instance, Park and Kim [91] described an optimal database construction of human re-targeted motion to learn arm motion primitives for a real-time human-like trajectory planning.  ... 
doi:10.3390/robotics9040102 fatcat:fahjlaf6hraslknxo7ifw5o4au

Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration [article]

Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
2018 arXiv   pre-print
partner from his or her ongoing motion, and re-plans its own actions in real time.  ...  This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human  ...  It only re-plans its task allocation if an inconsistency or deadlock is reached.  ... 
arXiv:1802.01780v1 fatcat:44nsbunffbeqvebcgxcmu42ji4

Integrating human observer inferences into robot motion planning

Anca Dragan, Siddhartha Srinivasa
2014 Autonomous Robots  
We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible  ...  Our goal is to enable robots to produce motion that is suitable for human-robot collaboration and coexistence.  ...  Acknowledgements We thank Geoff Gordon, Jodi Forlizzi, Hendrik Christiansen, Kenton Lee, Chris Dellin, Alberto Rodriguez, and the members of the Personal Robotics Lab for fruitful discussion and advice  ... 
doi:10.1007/s10514-014-9408-x fatcat:ng2my767jfdgdawog3jsrlpajy

Effects of Robot Motion on Human-Robot Collaboration

Anca D. Dragan, Shira Bauman, Jodi Forlizzi, Siddhartha S. Srinivasa
2015 Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction - HRI '15  
Such motion is great in isolation, but collaboration affords a human who is watching the motion and making inferences about it, trying to coordinate with the robot to achieve the task.  ...  Most motion in robotics is purely functional, planned to achieve the goal and avoid collisions.  ...  INTRODUCTION This paper studies the role of motion in collaborations between humans and robots, and how planning robot motion that explicitly considers the inferences that the collaborator makes affects  ... 
doi:10.1145/2696454.2696473 dblp:conf/hri/DraganBFS15 fatcat:gds7t26mtvhcphge3xs7c2a4ne

Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics [article]

Yingdong Qian, Marta Kryven, Tao Gao, Hanbyul Joo, Josh Tenenbaum
2021 arXiv   pre-print
We describe Generative Body Kinematics model, which predicts human intention inference in this domain using Bayesian inverse planning and inverse body kinematics.  ...  How to build AI that understands human intentions, and uses this knowledge to collaborate with people?  ...  The use of model predictive control (MPPI) in this case is mainly to reduce the unnecessary movements of planned motion. Inverse planning as inference.  ... 
arXiv:2112.00903v1 fatcat:pwxazc6ewbcwdn7nbzvysbmkfa

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Silvia Proia, Raffaele Carli, Graziana Cavone, Mariagrazia Dotoli
2021 IEEE Transactions on Automation Science and Engineering  
., vol. 59, control and iterative replanning for predicting human reaching motions pp. 346–360, Oct. 2019.  ...  Berenson, “Goal set inverse optimal human–robot collaboration,” Robot. Comput.-Integr.  ... 
doi:10.1109/tase.2021.3131011 fatcat:w7zm7vlqfnhzheanevg6x3toxm

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

Jae Sung Park, Chonhyon Park, Dinesh Manocha
2017 Robotics: Science and Systems XIII  
We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning.  ...  We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace.  ...  RELATED WORKS In this section, we give a brief overview of prior work on human motion prediction, task planning for human-robot collaborations, and motion planning in environments shared with humans.  ... 
doi:10.15607/rss.2017.xiii.045 dblp:conf/rss/ParkPM17 fatcat:a3pgv2hi3zecfj4x4nwnrrkerq

Viewing Robot Navigation in Human Environment as a Cooperative Activity [chapter]

Harmish Khambhaita, Rachid Alami
2019 Distributed Autonomous Robotic Systems  
To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space.  ...  Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior  ...  Acknowledgements This work is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 688147 (MuMMER project).  ... 
doi:10.1007/978-3-030-28619-4_25 fatcat:66jbortttna5fjncueioccb2qi

Viewing Robot Navigation in Human Environment as a Cooperative Activity [article]

Harmish Khambhaita, Rachid Alami
2017 arXiv   pre-print
To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space.  ...  Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior  ...  Acknowledgements This work is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 688147 (MuMMER project).  ... 
arXiv:1708.01267v2 fatcat:7u6dwupq7fbk5d36ayixe33csq

I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction [article]

Jae Sung Park and Chonhyon Park and Dinesh Manocha
2017 arXiv   pre-print
We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning.  ...  We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks.  ...  RELATED WORK In this section, we give a brief overview of prior work on human motion prediction, task planning for human-robot collaborations, and motion planning in environments shared with humans.  ... 
arXiv:1608.04837v5 fatcat:64ygzlhu6fcrrjkqtlaf23xsju
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