Machine learning, specifically through Recurrent Neural Networks (RNNs) and Graph Neural Networks (GNNs), enhances robot vision by enabling sequential and relational understanding of scenes.

When we think about machine learning, our minds often jump to datacenters full of sweating, overheating GPUs. However, lighter-weight hardware can also be used to these ends, as demonstrated by [Ni… ...

Robot machine learning. Bartnik’s approach to training the robot was meticulous and innovative. He collected a point cloud from the spinning lidar sensor by manually driving the robot through a ...

The creator manually controlled the robot while collecting LIDAR measurements and control labels, amassing valuable data for training. The subsequent machine learning phase involved feature selection, ...

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually ...

Figure 1. Block diagram of the proposed framework for estimating patient Active Level of Participation (ALP) in robot-aided rehabilitation. The system integrates interaction control, a multimodal ...

Machine learning-based approaches for soft robot proprioception have recently gained popularity, in part due to the difficulties in modeling the relationship between sensor signals and robot shape.

This paper presents a comprehensive study on the integration and effects of machine learning assist in the remote operation of a nursing mobile robot, a crucial tool in the evolving landscape of ...

How MIT Tackled Shape-Shifting Robots. In the realm of robotics, traditional machine-learning methods like reinforcement learning are typically applied to robots with well-defined moving parts ...