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

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

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

H. Robertshaw, N. Fischer, L. Karstensen, B. Jackson, X. Chen, S. M. Hadi Sadati, et al.

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.

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