analiza aiqbench
This research advances safe human-robot collaboration by integrating model predictive control with impedance-based compliance for non-prehensile pushing tasks. By incorporating an energy tank passivity filter, the system effectively prevents uncontrolled force buildup during external interactions while maintaining precise trajectory tracking. The approach represents a significant step toward robust robotic manipulation in dynamic environments where physical contact is inevitable.
Compliant Non-Prehensile Pushing Manipulation
arXiv:2605.25672v2 Announce Type: replace Abstract: In this paper, we address the challenge of performing non-prehensile pushing operations with a compliant robotic manipulation system. To ensure safe operations in human-populated environments, robots must comply with external physical interactions and exhibit a passive behavior. To achieve this, we extend a state-of-the-art pushing model to integrate it with impedance-controlled robots. We develop a model predictive control framework built upon this model that enables performing compliant pushing through optimal modulation of the robot's position/velocity set-point, jointly realizing the required pushing force and contact point adaptation to obtain a desired object motion. However, external interactions may induce tracking errors causing a consequent potentially indefinite increase of the pushing force. To prevent this, we integrate an energy tank passivity filter that further modulates the robot velocity set-point to guarantee passivity and avoid uncontrolled energy buildup. The proposed method has been rigorously tested in simulation and validated through experiments on two different robotic systems, demonstrating passive compliance during human-robot interactions, and assessing trajectory tracking performance and robustness to variations in the object's physical parameters.
Abstract:In this paper, we address the challenge of performing non-prehensile pushing operations with a compliant robotic manipulation system. To ensure safe operations in human-populated environments, robots must comply with external physical interactions and exhibit a passive behavior. To achieve this, we extend a state-of-the-art pushing model to integrate it with impedance-controlled robots. We develop a model predictive control framework built upon this model that enables performing compliant pushing through optimal modulation of the robot's position/velocity set-point, jointly realizing the required pushing force and contact point adaptation to obtain a desired object motion. However, external interactions may induce tracking errors causing a consequent potentially indefinite increase of the pushing force. To prevent this, we integrate an energy tank passivity filter that further modulates the robot velocity set-point to guarantee passivity and avoid uncontrolled energy buildup. The proposed method has been rigorously tested in simulation and validated through experiments on two different robotic systems, demonstrating passive compliance during human-robot interactions, and assessing trajectory tracking performance and robustness to variations in the object's physical parameters.
Robotics (cs.RO)
arXiv:2605.25672 [cs.RO]
(or arXiv:2605.25672v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.25672 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 263 cuvinte
Subiecte conexe
I don't think Claude is misaligned in 'Agentic Misalignment Summer 2026 - Motivated Mislabeling'
LessWrong · 17 iul. 2026
Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning
arXiv · 12 oct. 2026
Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
arXiv · 16 iun. 2026
SOMA: Stack-of-Objects Manipulation using a Quadruped Robot
arXiv · 17 oct. 2026