most citedRisk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming

29 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2021

ReDUCE: Reformulation of Mixed Integer Programs using Data from Unsupervised Clusters for Learning Efficient Strategies

Xuan Lin, Gabriel I. Fernandez, Dennis W. Hong

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines learning methods on solver heuristics has show…

cs.RO2021

Designing Multi-Stage Coupled Convex Programming with Data-Driven McCormick Envelope Relaxations for Motion Planning

Xuan Lin, Min Sung Ahn, Dennis Hong

For multi-limbed robots, motion planning with posture and force constraints tends to be a difficult optimization problem due to nonlinearities, which also present extended solve ti…

cs.RO2021

Transition Motion Planning for Multi-Limbed Vertical Climbing Robots Using Complementarity Constraints

Jingwen Zhang, Xuan Lin, Dennis W Hong

In order to achieve autonomous vertical wall climbing, the transition phase from the ground to the wall requires extra consideration inevitably. This paper focuses on the contact s…

math.NA20211 cited

On anisotropic non-Lipschitz restoration model: lower bound theory and convergent algorithm

Chunlin Wu, Xuan Lin, Yufei Zhao

For nonconvex and nonsmooth restoration models, the lower bound theory reveals their good edge recovery ability, and related analysis can help to design convergent algorithms. Exis…

cs.RO202029 cited

Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming

Yuki Shirai, Xuan Lin, Yusuke Tanaka +2

We present a motion planning algorithm with probabilistic guarantees for limbed robots with stochastic gripping forces. Planners based on deterministic models with a worst-case unc…