Categories: FAANG

Humanoid Policy ~ Human Policy

Training manipulation policies for humanoid robots with
diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot demonstrations is labor-intensive, requiring expensive tele-operated data
collection which is difficult to scale. This paper investigates a more scalable data source, egocentric human demonstrations, to serve as cross-embodiment training data for robot learning. We mitigate the embodiment gap between humanoids and humans
from both the data and modeling perspectives. We collect an egocentric task-oriented dataset (PH2D)…

ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning

Humanoid robots have significant gaps in their sensing and perception, making it hard to perform motion planning in dense environments. To address this, we introduce ARMOR, a novel egocentric perception system that integrates both hardware and software, specifically incorporating wearable-like depth sensors for humanoid robots. Our distributed perception approach enhances…

February 14, 2025

In "FAANG"

Novel framework can create egocentric human demonstrations for imitation learning

One of the most promising approaches to teaching robots how to complete manual tasks such as cleaning dishes or preparing food is known as imitation learning. End-to-end imitation learning typically entails training a deep learning algorithm on raw videos, images and/or motion capture data of humans completing manual tasks.

November 30, 2024

In "AI/ML News"

MM-Ego: Towards Building Egocentric Multimodal LLMs

This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as there is a lack of QA data for egocentric video understanding, we automatically generate 7M high-quality QA samples for egocentric videos ranging from 30…

April 11, 2025

In "FAANG"

AI Generated Robotic Content