Results 191 to 200 of about 12,651,285 (254)
Safe and adaptive control of non-stationary stochastic systems via Lyapunov-constrained distributional reinforcement learning. [PDF]
Khaniki MAL, Mirzaee M, Moradi E.
europepmc +1 more source
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
How AI Can Advance Mathematical Biology: Opportunities, Challenges, and Future Directions. [PDF]
Rabiei K +3 more
europepmc +1 more source
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source
Conservative Numerical Framework for Fractional Stochastic Delay Modeling of CSF-1/EGF Tumor-Macrophage Aggregation. [PDF]
Mohammad M, Rihan F, Trounev A.
europepmc +1 more source
LLM‐Integrated Human–Robot Interaction System for Microrobots
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley +1 more source
Stochastic three-term conjugate gradient: a third-order curvature approximation correction algorithmic framework for machine learning. [PDF]
Liu J, Yuan G, Mo Z.
europepmc +1 more source
Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith +4 more
wiley +1 more source
Gillespie-based simulation and inference for non-Markovian stochastic reaction networks. [PDF]
Pélissier A +3 more
europepmc +1 more source
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano +3 more
wiley +1 more source

