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robot learning

8 articles · Page 1 of 1
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Sep 12, 2026 · Artificial Intelligence

How Robots Turn World Representations into Action: Jiajun Wu's ECCV 2026 Insights

Stanford professor Jiajun Wu's ECCV 2026 talk explores how structured world representations enable robots to act in complex physical environments, covering compositional skill learning, neural kinematics for zero-shot object interaction, video diffusion models for generating free demonstrations, and new benchmarks for evaluating embodied reasoning.

BenchmarksECCV 2026Embodied AI
0 likes · 35 min read
How Robots Turn World Representations into Action: Jiajun Wu's ECCV 2026 Insights
Machine Heart
Machine Heart
Sep 11, 2026 · Artificial Intelligence

PhyFilter: Physics-Based Feedback Lets Robots Generalize Without Massive Data

Researchers from Beihang University and NTU propose PhyFilter, a plug-and-play physics-informed filter that corrects neural network errors using real-time robot state feedback and known differential structures, enabling zero-shot generalization across quadrupeds, drones, aerial manipulators, and acceleration estimation with only flat-ground simulation training.

PhyFilteraerial manipulationdrone control
0 likes · 18 min read
PhyFilter: Physics-Based Feedback Lets Robots Generalize Without Massive Data
Data Party THU
Data Party THU
Aug 19, 2026 · Artificial Intelligence

Weights vs. Skills: How Robot Learning Shifts from Action Prediction to Self-Written Skills

This survey maps a decade of robot‑learning research onto a weight‑vs‑skill axis, classifies 77 representative systems into six technical branches, analyzes their trade‑offs, highlights emerging skill‑economy challenges, and proposes measurable metrics for future self‑improving robotic systems.

AI roboticscode-as-policyrobot learning
0 likes · 17 min read
Weights vs. Skills: How Robot Learning Shifts from Action Prediction to Self-Written Skills
Machine Heart
Machine Heart
Jun 13, 2026 · Artificial Intelligence

Zero‑Shot Dual‑Arm Robot Learning from 30 Minutes of Human Egocentric Video (HumanEgo)

HumanEgo shows that a single 30‑minute egocentric video captured with a wearable Aria camera can train a dual‑arm robot to achieve 92.5% success on four real‑world tasks, transfer zero‑shot across robots, cameras and environments, and outperform tele‑operation while requiring far less data.

Egocentric VideoHumanEgoflow matching
0 likes · 11 min read
Zero‑Shot Dual‑Arm Robot Learning from 30 Minutes of Human Egocentric Video (HumanEgo)
Machine Heart
Machine Heart
Jun 9, 2026 · Artificial Intelligence

How PSI Lab’s Three Award‑Winning Papers Define a Systematic Humanoid Robot Learning Framework

The PSI Lab at USC, led by Wang Yue, secured three CVPR 2026 awards—Psi‑0, PhysWorld and Humanoid Everyday—each tackling a distinct stage of humanoid robot learning: large‑scale human video pre‑training, embodiment‑aligned fine‑tuning, and physics‑aware world modeling, together forming a coherent data‑model‑prediction pipeline.

Embodied AIFoundation ModelsWorld Models
0 likes · 14 min read
How PSI Lab’s Three Award‑Winning Papers Define a Systematic Humanoid Robot Learning Framework
Machine Heart
Machine Heart
Jun 7, 2026 · Artificial Intelligence

DexJoCo: First High‑Difficulty Benchmark with 11 Dexterous Manipulation Tasks Covering Four Core Abilities

DexJoCo, a new MuJoCo‑based benchmark from the Chinese Academy of Sciences, introduces 11 complex dexterous‑hand tasks spanning tool use, bimanual collaboration, long‑horizon execution, and reasoning, and reveals that even state‑of‑the‑art robot learning models still struggle with reliable fine‑grained manipulation.

ACTDiffusion PolicyMuJoCo
0 likes · 7 min read
DexJoCo: First High‑Difficulty Benchmark with 11 Dexterous Manipulation Tasks Covering Four Core Abilities
Machine Heart
Machine Heart
May 16, 2026 · Artificial Intelligence

Why Robots Need World Models: A Joint Survey from Leading Institutions

This article surveys recent advances in robot world models, explaining why predictive models are essential for embodied intelligence, how they integrate with Vision‑Language‑Action systems, the various architectural approaches, benchmark trends, and the remaining challenges for reliable deployment.

BenchmarkSimulationVision-Language-Action
0 likes · 14 min read
Why Robots Need World Models: A Joint Survey from Leading Institutions
Machine Heart
Machine Heart
May 10, 2026 · Artificial Intelligence

Embodied AI Unveiled: Ted Xiao Revisits Three Eras of Robot Learning from Google RT‑1/2 to SayCan

In a detailed interview, Ted Xiao, former Google DeepMind researcher, walks through the existence‑proof, foundation‑model, and scaling eras of embodied robot learning, explaining the technical challenges, pivotal decisions, and the evolving role of large language and vision models in robotics.

Embodied AIFoundation ModelsReinforcement Learning
0 likes · 19 min read
Embodied AI Unveiled: Ted Xiao Revisits Three Eras of Robot Learning from Google RT‑1/2 to SayCan