9 papers
Gradient based Bilevel for Inverse Optimal Control, a Riemannian approach
Ahmed-Manaf Dahmani, Vincent Bonnet, David Daney +1
Inverse Optimal Control (IOC) aims to recover the cost function that explains observed trajectories as solutions of an optimal control problem. Classical IOC formulations rely on b…
Integrated Hierarchical Decision-Making in Inverse Kinematic Planning and Control
Kai Pfeiffer, Quan Zhang, Yuqing Chen +4
This work presents a novel and efficient nonlinear programming framework that tightly integrates hierarchical decision-making with whole-body inverse kinematic planning and control…
COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments
Ege Gursoy, Maxime Sabbah, Arthur Haffemayer +5
Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has…
Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring
Krzysztof Wojciechowski, Ege Gursoy, Arthur Haffemayer +4
Model Predictive Control (MPC) is widely used for torque-controlled robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industr…
Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
Sarmad Mehrdad, Maxime Sabbah, Vincent Bonnet +1
This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or post…
Biomechanically consistent real-time action recognition for human-robot interaction
Wanchen Li, Kahina Chalabi, Sabbah Maxime +5
This paper presents a novel framework for real-time human action recognition in industrial contexts, using standard 2D cameras. We introduce a complete pipeline for robust and real…