IEEE

Towards Feasible Dynamic Grasping: Leveraging Gaussian Process Distance Field, SE(3) Equivariance, and Riemannian Mixture Models

Abstract: This paper introduces a novel approach to improve robotic grasping in dynamic environments by integrating Gaussian Process Distance Fields (GPDF), SE(3) equivariant networks, and Riemannian ...
Interesting Engineering

New adaptive system lets robots replicate human touch with far less training data

Japanese researchers develop an adaptive robot motion system that enables human-like grasping using minimal training data.
IEEE

Variable Impedance Control Combining Reinforcement Learning and Gaussian Process Regression

Abstract: Variable Impedance Control (VIC) approaches offer effective means for enabling robots to perform physical interaction tasks safely and proficiently, by including time-varying gains within an ...
Tech Xplore on MSN

Adaptive motion system helps robots achieve human-like dexterity with minimal data

Despite rapid robotic automation advancements, most systems struggle to adapt their pre-trained movements to dynamic ...

Bacterial energy model reveals how antimicrobial resistance spreads in the environment

Bacteria can sneakily evade our best efforts at eradication by developing resistance to various pressures in their ...
AlphaGalileo

Bringing Human Dexterity to Robots by Combining Human Motion and Tactile Sensation

Researchers develop an adaptive motion system that allows robots to generate human-like movements with minimal data ...