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

29 citations · 42 across the 6 of their papers we have counts for

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cs.RO20231 cited

Design of a Jumping Control Framework with Heuristic Landing for Bipedal Robots

Jingwen Zhang, Junjie Shen, Yeting Liu +1

Generating dynamic jumping motions on legged robots remains a challenging control problem as the full flight phase and large landing impact are expected. Compared to quadrupedal ro…

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.RO202111 cited

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…

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…

cs.RO2020

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…

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…