107 citations · 186 across the 12 of their papers we have counts for
12 papers
ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
Zifan Xu, Ran Gong, Maria Vittoria Minniti +10
Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (e.g., through teleoperation) serve as…
AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning
Ran Gong, Xiaohan Zhang, Jinghuan Shang +11
Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has beco…
Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
Jad Abou-Chakra, Lingfeng Sun, Krishan Rana +5
We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, which lack the ability to safely test policies…
Theia: Distilling Diverse Vision Foundation Models for Robot Learning
Jinghuan Shang, Karl Schmeckpeper, Brandon B. May +4
Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification…
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…
Collision detection and identification for a legged manipulator
Jessie van Dam, Andreea Tulbure, Maria Vittoria Minniti +2
To safely deploy legged robots in the real world it is necessary to provide them with the ability to reliably detect unexpected contacts and accurately estimate the corresponding c…
Adaptive CLF-MPC With Application To Quadrupedal Robots
Maria Vittoria Minniti, Ruben Grandia, Farbod Farshidian +1
Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects…
Deep Measurement Updates for Bayes Filters
Johannes Pankert, Maria Vittoria Minniti, Lorenz Wellhausen +1
Measurement update rules for Bayes filters often contain hand-crafted heuristics to compute observation probabilities for high-dimensional sensor data, like images. In this work, w…
Passivity-based control for haptic teleoperation of a legged manipulator in presence of time-delays
Mattia Risiglione, Jean-Pierre Sleiman, Maria Vittoria Minniti +3
When dealing with the haptic teleoperation of multi-limbed mobile manipulators, the problem of mitigating the destabilizing effects arising from the communication link between the…
Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks
Maria Vittoria Minniti, Ruben Grandia, Kevin Fäh +2
Modern, torque-controlled service robots can regulate contact forces when interacting with their environment. Model Predictive Control (MPC) is a powerful method to solve the under…
A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation
Jean-Pierre Sleiman, Farbod Farshidian, Maria Vittoria Minniti +1
In this paper, we propose a whole-body planning framework that unifies dynamic locomotion and manipulation tasks by formulating a single multi-contact optimal control problem. We m…
Whole-Body MPC for a Dynamically Stable Mobile Manipulator
Maria Vittoria Minniti, Farbod Farshidian, Ruben Grandia +1
Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body o…