15 citations · 38 across the 8 of their papers we have counts for
10 papers · 1 filter
From Tabletop to Walking Bot: Deploying a VLA Model on a Legged Manipulator Robot
Giovanni Minelli, Antonio Langella, Giovanni B. Dessy +4
Vision-language-action (VLA) models are typically pretrained on broad, diverse datasets and subsequently fine-tuned on a comparatively small dataset tailored to a specific target r…
ZiMPedance: Impedance-Aware ZMP Modeling and Control for Payload Carrying with Quadruped Robots
Giovanni B. Dessy, Lorenzo Amatucci, Victor Barasuol +1
Load transportation with quadruped robots is strongly affected by the dynamics of the physical interface between the robot and the load. Passive spring-based arms provide a lightwe…
Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots
Lorenzo Amatucci, João Sousa-Pinto, Giulio Turrisi +3
This paper introduces a novel Model Predictive Control (MPC) implementation for legged robot locomotion that leverages GPU parallelization. Our approach enables both temporal and s…
MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots
Ylenia Nisticò, João Carlos Virgolino Soares, Lorenzo Amatucci +2
This paper introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped rob…
Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning
Ilyass Taouil, Lorenzo Amatucci, Majid Khadiv +4
Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combin…
Accelerating Model Predictive Control for Legged Robots through Distributed Optimization
Lorenzo Amatucci, Giulio Turrisi, Angelo Bratta +2
This paper presents a novel approach to enhance Model Predictive Control (MPC) for legged robots through Distributed Optimization. Our method focuses on decomposing the robot dynam…
On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion
Giulio Turrisi, Valerio Modugno, Lorenzo Amatucci +2
Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addres…
Fast Convex Visual Foothold Adaptation for Quadrupedal Locomotion
Shafeef Omar, Lorenzo Amatucci, Giulio Turrisi +2
This extended abstract provides a short introduction on our recently developed perception-based controller for quadrupedal locomotion. Compared to our previous approach based on Vi…
SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
Shafeef Omar, Lorenzo Amatucci, Victor Barasuol +2
We present a footstep planning policy for quadrupedal locomotion that is able to directly take into consideration a-priori safety information in its decisions. At its core, a learn…
Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control
Lorenzo Amatucci, Joon-Ha Kim, Jemin Hwangbo +1
In this work, a non-gaited framework for legged system locomotion is presented. The approach decouples the gait sequence optimization by considering the problem as a decision-makin…