Tagged articles

robotic manipulation

12 articles · Page 1 of 1

Aug 4, 2026 · Artificial Intelligence

What Data Should Robots Learn From? A Five‑Layer Data Pyramid Framework

The paper proposes a five‑layer Data Pyramid for embodied manipulation, analyzing each layer—real‑robot, UMI‑style, ego‑centric, simulation, and generic vision‑language data—through quality, diversity, reusability and physical fidelity dimensions, and discusses how these layers can be combined for training embodied AI models.

Embodied AIMultimodal DataUMI

0 likes · 15 min read

What Data Should Robots Learn From? A Five‑Layer Data Pyramid Framework

Machine Heart

Jul 23, 2026 · Artificial Intelligence

EgoSteer: System for Dual Dexterous Hands Using Human Data and World Models

EgoSteer is an open‑source full‑stack framework that transforms massive first‑person human‑hand videos into high‑quality training data via the EgoSmith pipeline, trains a flow‑matching VLA augmented with a lightweight world‑model expert, and integrates a unified action space and real‑time chunking to enable dual dexterous hands to follow natural language commands, achieving up to 75 % success across 40 diverse manipulation tasks.

EgoSteerFew-Shot Learningdata pipeline

0 likes · 17 min read

EgoSteer: System for Dual Dexterous Hands Using Human Data and World Models

Machine Heart

Jul 17, 2026 · Artificial Intelligence

Can General-Purpose Robots Arrive Soon? Inside the RoboDojo “Embodied Everest” Benchmark

The RoboDojo benchmark evaluates 30 robot manipulation policies across 42 simulated and 18 real-world tasks, revealing that the best models achieve only 8.8% success in simulation and 12.8% in reality, far behind human experts, and highlights gaps in generalization, memory, precision, long‑horizon execution, and open‑semantic understanding.

RoboDojoreal-world evaluationrobotic manipulation

0 likes · 9 min read

Can General-Purpose Robots Arrive Soon? Inside the RoboDojo “Embodied Everest” Benchmark

Machine Heart

Jul 17, 2026 · Artificial Intelligence

How RynnWorld‑4D Gives Robots a 3D‑Future Vision with 4D World Modeling

The article analyzes the limitations of 2D video‑based world models for robotic manipulation, introduces the RGB‑DF 4D representation and a three‑branch Transformer with joint cross‑modal attention, details a staged training pipeline and a massive 4D dataset, and demonstrates superior geometry, motion, and policy performance on real‑world dual‑arm tasks.

4D world modelRGB-DF representationdiffusion model

0 likes · 14 min read

How RynnWorld‑4D Gives Robots a 3D‑Future Vision with 4D World Modeling

Machine Heart

Jul 15, 2026 · Artificial Intelligence

World Models Enter the Real Testbed: WorldArena 2.0 Challenge Launched

The WorldArena 2.0 Challenge expands world‑model evaluation from offline video quality to online reinforcement‑learning loops and real‑robot tasks, introducing three tracks that test physical consistency, multimodal perception, and closed‑loop execution on diverse robotic platforms.

BenchmarkEmbodied AIWorldArena

0 likes · 13 min read

World Models Enter the Real Testbed: WorldArena 2.0 Challenge Launched

Machine Heart

Jun 26, 2026 · Artificial Intelligence

LabVLA: Bridging AI Reasoning and Hands‑On Lab Automation

LabVLA introduces a vision‑language‑action framework and a knowledge‑enhanced simulation engine to enable AI models to learn and generalize scientific lab manipulation, achieving 71% success on benchmark tasks and demonstrating real‑world performance on a Franka robot, while outlining current limitations and future directions.

AI for scienceEmbodied AILabVLA

0 likes · 12 min read

LabVLA: Bridging AI Reasoning and Hands‑On Lab Automation

Machine Learning Algorithms & Natural Language Processing

May 12, 2026 · Artificial Intelligence

LaST‑R1: Embodied Robot Model Hits 99.9% LIBERO Success via Physical Reasoning

LaST‑R1 presents a new embodied AI framework that inserts latent physical reasoning before action generation and jointly optimizes reasoning and control with LAPO, achieving 99.9% average success on the LIBERO benchmark after a single‑trajectory warm‑up and boosting real‑world task success from 52.5% to 93.75%, while showing superior generalization to unseen objects, backgrounds and lighting.

Embodied AILAPOLIBERO Benchmark

0 likes · 11 min read

LaST‑R1: Embodied Robot Model Hits 99.9% LIBERO Success via Physical Reasoning

Machine Heart

May 7, 2026 · Artificial Intelligence

Genesis AI Shows Embodied Model That Cooks, Experiments and Plays Piano

Genesis AI’s new GENE‑26.5 embodied foundation model demonstrates long‑horizon robot capabilities—from cooking a multi‑step meal and solving a Rubik’s cube to playing a high‑speed piano piece—using a full‑stack system that combines human‑like hands, a data‑glove, extensive simulation, and ultra‑low‑latency control.

Data GloveEmbodied AIfoundation model

0 likes · 11 min read

Genesis AI Shows Embodied Model That Cooks, Experiments and Plays Piano

Machine Heart

Apr 11, 2026 · Artificial Intelligence

How 100,000 Hours of Human Data Propelled Psi‑R2 to Lead MolmoSpaces

Lingchu AI demonstrates that scaling human‑operation data to nearly 100,000 hours, combined with a two‑model system and reinforcement learning, can replace costly robot‑teleoperation data and achieve top performance on the MolmoSpaces benchmark.

Embodied AIPsi-R2Psi-W0

0 likes · 12 min read

How 100,000 Hours of Human Data Propelled Psi‑R2 to Lead MolmoSpaces

Xiaomi Tech

Feb 5, 2026 · Artificial Intelligence

TacRefineNet: A Tactile‑Driven Model for Millimeter‑Precision Robotic Grasp Refinement

TacRefineNet leverages high‑resolution tactile sensors, multimodal fusion of fingertip touch and proprioception, and a goal‑conditioned refinement network to achieve millimeter‑level grasp adjustments without vision or 3D models, demonstrating zero‑shot deployment and robust generalization across diverse automotive‑factory parts in both simulation and real‑world tests.

Multimodal FusionSim-to-Realgrasp refinement

0 likes · 8 min read

TacRefineNet: A Tactile‑Driven Model for Millimeter‑Precision Robotic Grasp Refinement

Network Intelligence Research Center (NIRC)

Jul 30, 2025 · Artificial Intelligence

Understanding Dexterous Hand Grasping in Embodied Intelligence

Dexterous hand grasping, a core challenge in embodied intelligence due to high degrees of freedom, is categorized into three approaches—ego-centric, object-centric, and ego-object interaction—each with distinct trade-offs, and the article cites representative works such as UniDexGrasp, GraspTTA, and DRO_Grasp.

Embodied IntelligenceUniDexGraspdexterous grasping

0 likes · 3 min read

Understanding Dexterous Hand Grasping in Embodied Intelligence

AntTech

Oct 29, 2024 · Artificial Intelligence

Embodied Intelligence and General‑Purpose Humanoid Robots: Insights from Wang He’s Ant T‑Space Talk

In a detailed presentation, Peking University assistant professor Wang He explained the current state and future direction of embodied intelligence, emphasizing synthetic data, three core intelligences, and the commercial‑grade capabilities of his startup’s general‑purpose humanoid robots across manufacturing, retail, and home applications.

Embodied AIHumanoid Robotindustrial automation

0 likes · 17 min read

Embodied Intelligence and General‑Purpose Humanoid Robots: Insights from Wang He’s Ant T‑Space Talk