Conference item
Preview optimization for learning locomotion policies on rough terrain
- Abstract:
-
Legged robots promise a clear advantage in unstructured and challenging terrain, scenarios such as disaster relief, search and rescue, forestry and construction site. Dynamic locomotion on rough terrain has to guarantee stability and maximizing the cross-ability of a local set of candidate footholds. Trajectory optimization improves such performance metric while satisfying locomotion stability. Terrain conditions increase significantly the dimensionality of the optimization problem. Moreover,...
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- Publication status:
- Not published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Host title:
- Workshop on Dynamic Locomotion and Manipulation with Complex Robotic Systems in the Real World (DLMC 2016)
- Journal:
- Workshop on Dynamic Locomotion and Manipulation with Complex Robotic Systems in the Real World (DLMC 2016) Journal website
- Publication date:
- 2016-07-14
- Acceptance date:
- 2016-06-03
Item Description
- Pubs id:
-
pubs:822553
- UUID:
-
uuid:35dcc960-2ae9-438c-bdfb-1a1ea85c04c2
- Local pid:
- pubs:822553
- Source identifiers:
-
822553
- Deposit date:
- 2018-02-02
Terms of use
- Copyright holder:
- Mastalli et al
- Copyright date:
- 2016
- Notes:
- This paper was presented at the Workshop on Dynamic Locomotion and Manipulation with Complex Robotic Systems in the Real World (DLMC 2016), 13-15 July 2016, Zurich.
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