Toward AI Autonomous Navigation for Mechanical Thrombectomy Using Hierarchical Modular Multi-Agent Reinforcement Learning (HM-MARL)

Integrated by Design: A Hybrid Robotic Palm Fusing a Functionally-Dense Compliant Matrix With an Actuated Skeleton

O. Neumann, L. Ewering, Robert K. Katzschmann

Inspired by the human hand, this work presents a hybrid soft-rigid architecture for a robotic palm to enhance dexterity and robustness. While soft-robotic hands often lack the structural integrity to handle heavy objects, classical rigid designs are sensitive to impacts and frequ…Read more

CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion

Ralf Römer, Yi Zhang, Yuming Li, Angela P. Schoellig

Differentiable Weights-Varying Nonlinear MPC via Gradient-Based Policy Learning: An Autonomous Vehicle Guidance Example

Felix Jahncke, Baha Zarrouki, Mattia Piccinini, Jovin D'sa, David Isele, Sangjae Bae, et al.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles With Deep Reinforcement Learning

Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez Reina, Sergio L. Toral Marín

CHAI – Compliant Human-centered Adaptive Interaction through Diffusion-based Language Trajectory Transformer

Junhui Huang, Xingguang Duan, A. Bucker, Changsheng Li, L. Figueredo

TL;DR: CHAI (Compliant Human-centered Adaptive Interaction), a novel language-driven framework for real-time modulation of a robot’s kinematics and mechanical compliance in real-world environments, introduces on-the-fly language-driven impedance (compliance) modulation along both translational and rotational directions.

We present CHAI (Compliant Human-centered Adaptive Interaction), a novel language-driven framework for real-time modulation of a robot’s kinematics and mechanical compliance in real-world environments. By interpreting natural language instructions and visual context through pre-t…Read more