Real-Time Motion Prediction for Efficient Human-Robot Collaboration
Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a mod...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/152639 |
| _version_ | 1826193274493206528 |
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| author | Kothari, Aadi |
| author2 | Youcef-Toumi, Kamal |
| author_facet | Youcef-Toumi, Kamal Kothari, Aadi |
| author_sort | Kothari, Aadi |
| collection | MIT |
| description | Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a model encompassing human motion. While these methods provide good initial results, they are missing out on leveraging well-studied human body kinematic models as well as body and scene constraints which can help boost the efficacy of these prediction frameworks while also explicitly avoiding implausible human joint configurations. We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon. This formulation is combined with an online context-aware constraints model to leverage task-dependent motions. It is tested on a human arm kinematic model and implemented on a human-robot collaborative setup with a UR5 robot arm to demonstrate the real-time capability of our approach. Simulations were also performed on datasets like HA4M and ANDY. The simulation and experimental results demonstrate considerable improvements in a Gaussian Process framework when these constraints are explicitly considered. |
| first_indexed | 2024-09-23T09:36:45Z |
| format | Thesis |
| id | mit-1721.1/152639 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:36:45Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1526392023-11-03T03:56:19Z Real-Time Motion Prediction for Efficient Human-Robot Collaboration Kothari, Aadi Youcef-Toumi, Kamal Massachusetts Institute of Technology. Department of Mechanical Engineering Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a model encompassing human motion. While these methods provide good initial results, they are missing out on leveraging well-studied human body kinematic models as well as body and scene constraints which can help boost the efficacy of these prediction frameworks while also explicitly avoiding implausible human joint configurations. We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon. This formulation is combined with an online context-aware constraints model to leverage task-dependent motions. It is tested on a human arm kinematic model and implemented on a human-robot collaborative setup with a UR5 robot arm to demonstrate the real-time capability of our approach. Simulations were also performed on datasets like HA4M and ANDY. The simulation and experimental results demonstrate considerable improvements in a Gaussian Process framework when these constraints are explicitly considered. S.M. 2023-11-02T20:04:51Z 2023-11-02T20:04:51Z 2023-09 2023-09-28T15:50:02.655Z Thesis https://hdl.handle.net/1721.1/152639 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Kothari, Aadi Real-Time Motion Prediction for Efficient Human-Robot Collaboration |
| title | Real-Time Motion Prediction for Efficient Human-Robot
Collaboration |
| title_full | Real-Time Motion Prediction for Efficient Human-Robot
Collaboration |
| title_fullStr | Real-Time Motion Prediction for Efficient Human-Robot
Collaboration |
| title_full_unstemmed | Real-Time Motion Prediction for Efficient Human-Robot
Collaboration |
| title_short | Real-Time Motion Prediction for Efficient Human-Robot
Collaboration |
| title_sort | real time motion prediction for efficient human robot collaboration |
| url | https://hdl.handle.net/1721.1/152639 |
| work_keys_str_mv | AT kothariaadi realtimemotionpredictionforefficienthumanrobotcollaboration |