116 citations · 1.1k across the 36 of their papers we have counts for
8 papers · 1 filter
Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation
Elena Arcari, Maria Vittoria Minniti, Anna Scampicchio +4
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-prin…
Perceptive Locomotion through Nonlinear Model Predictive Control
Ruben Grandia, Fabian Jenelten, Shaohui Yang +2
Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such mo…
Haptic Teleoperation of High-dimensional Robotic Systems Using a Feedback MPC Framework
Jin Cheng, Firas Abi-Farraj, Farbod Farshidian +1
Model Predictive Control (MPC) schemes have proven their efficiency in controlling high degree-of-freedom (DoF) complex robotic systems. However, they come at a high computational…
TAMOLS: Terrain-Aware Motion Optimization for Legged Systems
Fabian Jenelten, Ruben Grandia, Farbod Farshidian +1
Terrain geometry is, in general, non-smooth, non-linear, non-convex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This work presents…
Whole-Body MPC and Dynamic Occlusion Avoidance: A Maximum Likelihood Visibility Approach
Ibrahim Ibrahim, Farbod Farshidian, Jan Preisig +3
This paper introduces a novel approach for whole-body motion planning and dynamic occlusion avoidance. The proposed approach reformulates the visibility constraint as a likelihood…
Planar Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics
Manuel Y. Galliker, Noel Csomay-Shanklin, Ruben Grandia +4
The ability to generate dynamic walking in real-time for bipedal robots with input constraints and underactuation has the potential to enable locomotion in dynamic, complex and uns…
A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation
Jia-Ruei Chiu, Jean-Pierre Sleiman, Mayank Mittal +2
In this paper, we present a real-time whole-body planner for collision-free legged mobile manipulation. We enforce both self-collision and environment-collision avoidance as soft c…
Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators
Yuntao Ma, Farbod Farshidian, Takahiro Miki +2
Deep reinforcement learning produces robust locomotion policies for legged robots over challenging terrains. To date, few studies have leveraged model-based methods to combine thes…