19 citations · 32 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
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…