19 citations · 32 across the 15 of their papers we have counts for
15 papers
Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation
Valerio Belli, Valerio Modugno, Enrico Mingo Hoffman +1
Reinforcement learning can produce robust humanoid controllers, but each new task is typically trained as a separate policy with its own reward design and training process. Motion…
Characterizing Optimizer-Dependent Training Dynamics Through Hessian Eigenvector Displacement and Localization
Marcelina Marjankowska, Valerio Modugno, Paolo Barucca
Hessian spectral properties are a standard tool in analysing neural-network training, with eigenvalues linked to sharpness, generalization, and optimization dynamics. Eigenvalues q…
Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approach
Antonio Marino, Valerio Modugno, Marco Cognetti
Actor-Critic Model Predictive Control (MPC) effectively addresses complex, non-convex control problems, but guaranteeing the closed-loop stability of sequence-based learning models…
A Progress-Aware Leader-Follower Midair Docking System for Dual-Drone Aerial Manipulation
Yifan Cai, Jan Ming Kevin Tan, Xiangqi Li +3
Reliable midair docking between small unmanned aerial vehicles (UAVs) is essential for modular aerial cooperation and manipulation, but it requires precise relative-pose control an…
An Aerial Manipulator for Perception-Driven Flower Targeting Toward Contactless Pollination in Vertical Farming
Chenzhe Jin, Zhuohang Wu, Yifan Cai +4
The decline of natural pollinators has created a major challenge for crop production in controlled indoor agriculture, particularly in vertical farming environments where natural i…
BC-MPPI: A Probabilistic Constraint Layer for Safe Model-Predictive Path-Integral Control
Odichimnma Ezeji, Michael Ziegltrum, Giulio Turrisi +2
Model Predictive Path Integral (MPPI) control has recently emerged as a fast, gradient-free alternative to model-predictive control in highly non-linear robotic tasks, yet it offer…
Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation -- Adios low-level controllers
Tommaso Belvedere, Michael Ziegltrum, Giulio Turrisi +1
Model Predictive Path Integral control is a powerful sampling-based approach suitable for complex robotic tasks due to its flexibility in handling nonlinear dynamics and non-convex…
Immersive Teleoperation Framework for Locomanipulation Tasks
Takuya Boehringer, Jonathan Embley-Riches, Karim Hammoud +2
Recent advancements in robotic loco-manipulation have leveraged Virtual Reality (VR) to enhance the precision and immersiveness of teleoperation systems, significantly outperformin…
On-Line Learning for Planning and Control of Underactuated Robots with Uncertain Dynamics
Giulio Turrisi, Marco Capotondi, Claudio Gaz +3
We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the…
DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding
Wenhua Wu, Guangming Wang, Ting Deng +5
Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, there are still some c…
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…
ViT-A*: Legged Robot Path Planning using Vision Transformer A*
Jianwei Liu, Shirui Lyu, Denis Hadjivelichkov +2
Legged robots, particularly quadrupeds, offer promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance. This pape…
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies
Dionis Totsila, Konstantinos Chatzilygeroudis, Denis Hadjivelichkov +3
State-of-the-art sensorimotor learning algorithms offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Traditional robot learning, o…
Local Path Planning among Pushable Objects based on Reinforcement Learning
Linghong Yao, Valerio Modugno, Andromachi Maria Delfaki +3
In this paper, we introduce a method to deal with the problem of robot local path planning among pushable objects -- an open problem in robotics. In particular, we achieve that by…
Gait Generation using Intrinsically Stable MPC in the Presence of Persistent Disturbances
Filippo M. Smaldone, Nicola Scianca, Valerio Modugno +2
Maintaining balance while walking is not a simple task for a humanoid robot because of its complex dynamics. The presence of a persistent disturbance makes this task even more chal…