Dyadic collaborative Manipulation through Hybrid Trajectory Optimization
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 118092804
- Type
- E - Conference contribution
- DOI
-
-
- Title of conference / published proceedings
- Proceedings of The 2nd Conference on Robot Learning
- First page
- 869
- Volume
- -
- Issue
- -
- ISSN
- 2640-3498
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
http://proceedings.mlr.press/v87/stouraitis18a.html
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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D - Language, Interaction and Robotics
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, for the first time, enables real-time optimisation of hybrid characteristics (e.g., grasp location, changes & timing, manipulation force, dyadic trajectories) in a human robot collaborative task. Was selected for oral presentation (2.4% acceptance rate) and nominated Finalist for the Best System Paper at CoRL 2018. The output of this work (published as an IEEE TRO paper) has been tested and deployed at the HONDA Research Labs for trialing in their collaborative assembly lines (contact: Chief Scientist). This work forms the basis of a new project initiative with the AIRS (Artificial Intelligence and Robotics for Society) Lab in CUHK-Shenzhen.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -