29 citations · 42 across the 6 of their papers we have counts for
7 papers
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…
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots
Alexander Schperberg, Stephanie Tsuei, Stefano Soatto +1
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in…
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…
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance
Alexander Schperberg, Kenny Chen, Stephanie Tsuei +5
In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navig…
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…
Deep Reinforcement Learning with Linear Quadratic Regulator Regions
Gabriel I. Fernandez, Colin Togashi, Dennis W. Hong +1
Practitioners often rely on compute-intensive domain randomization to ensure reinforcement learning policies trained in simulation can robustly transfer to the real world. Due to u…