Human control of complex objects: towards more dexterous robots. (1st September 2020)
- Record Type:
- Journal Article
- Title:
- Human control of complex objects: towards more dexterous robots. (1st September 2020)
- Main Title:
- Human control of complex objects: towards more dexterous robots
- Authors:
- Bazzi, Salah
Sternad, Dagmar - Abstract:
- Abstract : Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at 'tool use' and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex – underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human–robotAbstract : Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at 'tool use' and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex – underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human–robot interaction. GRAPHICAL ABSTRACT: UF0001 … (more)
- Is Part Of:
- Advanced robotics. Volume 34:Number 17(2020)
- Journal:
- Advanced robotics
- Issue:
- Volume 34:Number 17(2020)
- Issue Display:
- Volume 34, Issue 17 (2020)
- Year:
- 2020
- Volume:
- 34
- Issue:
- 17
- Issue Sort Value:
- 2020-0034-0017-0000
- Page Start:
- 1137
- Page End:
- 1155
- Publication Date:
- 2020-09-01
- Subjects:
- Complex object manipulation -- human motor control -- underactuated -- chaos -- stability
Robotics -- Periodicals
Robotics -- Japan -- Periodicals
Robotics
Japan
Periodicals
629.89205 - Journal URLs:
- http://www.catchword.com/rpsv/cw/vsp/01691864/contp1.htm ↗
http://catalog.hathitrust.org/api/volumes/oclc/14883000.html ↗
http://www.tandfonline.com/toc/tadr20/current ↗
http://www.tandfonline.com/ ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0169-1864;screen=info;ECOIP ↗
http://www.ingentaselect.com/vl=16659242/cl=11/nw=1/rpsv/cw/vsp/01691864/contp1.htm ↗ - DOI:
- 10.1080/01691864.2020.1777198 ↗
- Languages:
- English
- ISSNs:
- 0169-1864
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.926500
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 22706.xml