AIConfPaper
← Search

Chadi Salmi

4 accepted papers

2025

Sampling-Based Model Predictive Control Leveraging Parallelizable Physics Simulations

Corrado Pezzato, Chadi Salmi, Elia Trevisan, Max Spahn, Javier Alonso-Mora, Carlos Hernández Corbato
RA-L 2025

We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of the robot a

Cited by 15Source ↗code ↗Scholar ↗
2024

Demonstrating Adaptive Mobile Manipulation in Retail Environments

Max Spahn, Corrado Pezzato, Chadi Salmi, Rick Dekker, Cong Wang, Christian Pek +4
RSS 2024poster

Although autonomous robots have great potential to boost efficiency and throughput across the whole retail chain, they are mostly being deployed in large warehouses and distribution centers. Deploying robots in stores with customers, such as supermarkets, requires substantially more development effo…

Cited by 5Source ↗PDF ↗Scholar ↗
2024

Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning

Yuezhe Zhang, Corrado Pezzato, Elia Trevisan, Chadi Salmi, Carlos Hernández Corbato, Javier Alonso-Mora
RA-L 2024

Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (A

Cited by 20Source ↗Scholar ↗
2022

Improving Pedestrian Prediction Models With Self-Supervised Continual Learning

Luzia Knödler, Chadi Salmi, Hai Zhu, Bruno Brito, Javier Alonso-Mora
RA-L 2022

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This letter introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online

Cited by 17Source ↗code ↗Scholar ↗