Discovering, Synthesizing, and Learning Climbing Movements

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Kyrki, Ville, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland Naderi, Kourosh 2020-05-19T09:00:12Z 2020-05-19T09:00:12Z 2020
dc.identifier.isbn 978-952-60-8887-7 (electronic)
dc.identifier.isbn 978-952-60-8886-0 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.description The public defense on 8th June 2020 at 12:00 will be available via remote technology. Link: Zoom Quick Guide:
dc.description.abstract Although animation is commonly captured from real humans or edited manually through keyframes and interpolation curves, such techniques are time-consuming and expensive. Because of this, a long-standing goal of computer animation research has been to synthesize movements algorithmically, e.g., through simulating the human biomechanics and framing the animation problem as optimization of joint torques over time. During recent years, such approaches have enjoyed success with movements like bipedal locomotion, but some complex movement skills have remained challenging. This dissertation focuses on synthesizing natural looking and human-like climbing movements in indoor bouldering, a popular and rapidly growing sport that recently was approved to the 2020 Tokyo Olympics together with speed and sport climbing. Indoor bouldering is a form of climbing that takes place relatively close to the ground on top of a soft landing surface, and does not require special equipment other than climbing shoes. Bouldering routes are usually short and they focus on complex climbing moves that require both strength and coordination. Planning and discovering the optimal or at least possible sequence of moves from the ground to the top hold is a challenging problem. The problem gets even more complicated when the planning should consider the body types of users such that the planned path and synthesized motions would be feasible for them. This thesis proposes a high-level path planner and low-level controller for synthesizing physically plausible and human-like movements. The high-level graph-based path planner is responsible for planning a sequence of movements to the top hold while the low-level controller synthesizes the movement details through optimizing the joint actuations of a physics simulation model of a humanoid climber. Such a low-level controller might fail to follow the planned movements; the thesis proposes ways to handle this uncertainty through low-level and high-level controller interaction. In subsequent work, the approach is developed further by employing neural networks in both supervised and reinforcement learning settings. The methods proposed in the thesis result in high-quality climbing animations without needing any reference animation or motion capture data. The work should also have applications in synthesizing other types of movements with similar characteristics, e.g., creating parkour animations based on desired footstep patterns. en
dc.format.extent 97 + app. 49
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 87/2020
dc.relation.haspart [Publication 1]: Kourosh Naderi, Joose Rajamäki, Perttu Hämäläinen. Discovering and Synthesizing Humanoid Climbing Movements. ACM Transactions on Graphics (TOG), 36, 4, Article 43, 11 pages, July 2017. DOI: 10.1145/3072959.3073707
dc.relation.haspart [Publication 2]: Kourosh Naderi, Jari Takatalo, Jari Lipsanen, Perttu Hämäläinen. Computer-Aided Imagery in Sport and Exercise: A Case Study of Indoor Wall Climbing. In Proceedings of Graphics Interface (GI), pp. 93 - 99, Toronto, Canada , May 2018. Full text in Acris/Aaltodoc: DOI: 10.20380/GI2018.13
dc.relation.haspart [Publication 3]: Kourosh Naderi, Amin Babadi, Perttu Hämäläinen. Learning Physically Based Humanoid Climbing Movements. In Computer Graphics Forum, vol. 37, no. 8, pp. 69-80, December 2018. DOI: 10.1111/cgf.13513
dc.relation.haspart [Publication 4]: Kourosh Naderi, Amin Babadi, Shaghayegh Roohi, Perttu Hämäläinen. A Reinforcement Learning Approach to Synthesizing Climbing Movements. In IEEE Conference on Games (CoG), London, UK, August 2019. Full text in Acris/Aaltodoc: DOI: 10.1109/CIG.2019.8848127
dc.relation.haspart [Errata file]: Errata of P1, P4
dc.subject.other Computer science en
dc.title Discovering, Synthesizing, and Learning Climbing Movements en
dc.type G5 Artikkeliväitöskirja fi Perustieteiden korkeakoulu fi School of Science en
dc.contributor.department Tietotekniikan laitos fi
dc.contributor.department Department of Computer Science en
dc.subject.keyword hierarchical motion planning en
dc.subject.keyword graph search en
dc.subject.keyword sampling-based movement optimization en
dc.subject.keyword supervised learning en
dc.subject.keyword reinforcement learning en
dc.identifier.urn URN:ISBN:978-952-60-8887-7
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Hämäläinen, Perttu, Prof, Aalto University, Department of Computer Science, Finland
dc.opn LaValle, Steven, Prof., University of Oulu, Finland
dc.contributor.lab Aalto Game Research Group en
dc.rev Andrews, Sheldon, Prof., École de technologie supérieure, Canada
dc.rev Tonneau, Steve, Prof., University of Edinburgh, UK 2020-06-08
local.aalto.acrisexportstatus checked 2020-06-23_2214
local.aalto.formfolder 2020_05_19_klo_11_10
local.aalto.archive yes

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication