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Imitation Learning

17 articles · Page 1 of 1
Machine Heart
Machine Heart
Oct 3, 2026 · Artificial Intelligence

LeaP: Learning Where Robot Action Generation Should Begin

LeaP introduces a learnable source prior conditioned on proprioception that jointly predicts mean and variance of a diagonal Gaussian for initializing generative robot policies, achieving 81.6% average success on 15 RoboTwin tasks (25.5 pp over standard Gaussian) and 80% on real Franka tasks (33.3 pp gain), with faster convergence.

CoRL 2026Flow MatchingGenerative Robot Policies
0 likes · 11 min read
LeaP: Learning Where Robot Action Generation Should Begin
Machine Heart
Machine Heart
Sep 9, 2026 · Artificial Intelligence

Action Map Policy: Pixel Classification for High-Precision Robot Manipulation

Action Map Policy (AMP) reframes robot manipulation as pixel-level classification by projecting 3D keypoint trajectories onto 2D image planes, enabling cross-entropy loss to model multi-modal action distributions with sub-millimeter precision and single-forward-pass inference.

Action Map PolicyCross-EntropyImitation Learning
0 likes · 12 min read
Action Map Policy: Pixel Classification for High-Precision Robot Manipulation
Data Party THU
Data Party THU
Aug 22, 2026 · Artificial Intelligence

RL‑100 Merges Imitation and Reinforcement Learning for High‑Performance Robot Manipulation

The RL‑100 framework combines imitation learning from human tele‑operation with offline and online reinforcement learning to refine diffusion‑based control policies, achieving 100 % success across eight real‑world robot tasks, matching or surpassing human operators in speed while maintaining stability and low latency.

Imitation LearningRL-100diffusion models
0 likes · 7 min read
RL‑100 Merges Imitation and Reinforcement Learning for High‑Performance Robot Manipulation
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Aug 7, 2026 · Artificial Intelligence

World Action Models: VLA + World Model‑Driven Paradigm Shift for Physical AI Post‑Training

The article analyzes the fundamental bottleneck of current Physical AI post‑training, where VLA models lack foresight, and presents World Action Models (WAM) – both Cascaded and Joint architectures – along with four post‑training pathways that combine evaluation and imagination to break the data‑collection loop.

Imitation LearningPhysical AISimulation
0 likes · 18 min read
World Action Models: VLA + World Model‑Driven Paradigm Shift for Physical AI Post‑Training
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 AIImitation Learningfoundation models
0 likes · 19 min read
Embodied AI Unveiled: Ted Xiao Revisits Three Eras of Robot Learning from Google RT‑1/2 to SayCan
Machine Heart
Machine Heart
Apr 5, 2026 · Artificial Intelligence

How Imitation Learning Powers Dexterous Manipulation: A 2021‑2025 Technical Roadmap

This survey maps the 2021‑2025 progress of imitation learning for dexterous manipulation, detailing theoretical foundations, datasets, algorithms, hardware platforms, and evaluation protocols, and highlights challenges such as data quality, hardware dependence, and the need for standardized benchmarks to advance embodied AI.

AlgorithmsImitation Learningdatasets
0 likes · 11 min read
How Imitation Learning Powers Dexterous Manipulation: A 2021‑2025 Technical Roadmap
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 24, 2026 · Artificial Intelligence

How an Interactive Imitation‑Learning Agent Framework Trains Robust Trading Strategies

The article analyzes the simulation‑reality gap in algorithmic trading and proposes an interactive market simulator that combines a pool of imitation‑learning agents, an action‑synthesis network, and a DDPG‑based reinforcement‑learning trader, showing superior robustness and downside protection on QQQ data.

Agent-based ModelingDDPGImitation Learning
0 likes · 16 min read
How an Interactive Imitation‑Learning Agent Framework Trains Robust Trading Strategies
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Nov 10, 2025 · Artificial Intelligence

How to Boost Robot Imitation Learning with Cosmos World Model Data Augmentation

This guide demonstrates an end‑to‑end workflow on Alibaba Cloud PAI that uses the Cosmos world model to replace Isaac simulation for robot action data augmentation, including minimal human demonstrations, prompt‑driven data expansion, rejection sampling, IDM inverse‑kinematics extraction, imitation‑learning fine‑tuning, and model evaluation.

AICosmosData Augmentation
0 likes · 17 min read
How to Boost Robot Imitation Learning with Cosmos World Model Data Augmentation
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Nov 3, 2025 · Artificial Intelligence

Build Physical AI with Isaac Lab: Data Augmentation, Imitation Learning & Evaluation

This article walks through an end‑to‑end Physical AI workflow on Alibaba Cloud PAI, covering robot teleoperation data collection, Isaac Lab‑based data augmentation and enhancement, imitation‑learning model training, distributed DLC execution, and systematic evaluation across varied visual conditions.

Data AugmentationImitation LearningPhysical AI
0 likes · 17 min read
Build Physical AI with Isaac Lab: Data Augmentation, Imitation Learning & Evaluation
Data Party THU
Data Party THU
Aug 9, 2025 · Artificial Intelligence

Demystifying MaxEnt Inverse Reinforcement Learning: Theory, Algorithms, and Practical Implementation

This article provides a comprehensive, step‑by‑step exploration of MaxEnt Inverse Reinforcement Learning, covering its statistical foundations, feature‑expectation matching, algorithmic details, deep extensions, and practical engineering considerations for complex decision‑making tasks.

Deep IRLFeature MatchingImitation Learning
0 likes · 21 min read
Demystifying MaxEnt Inverse Reinforcement Learning: Theory, Algorithms, and Practical Implementation
AI Frontier Lectures
AI Frontier Lectures
May 31, 2025 · Artificial Intelligence

Why Embodied Intelligence Is Exploding and What It Means for the Future

The article analyzes the recent surge in embodied intelligence, examines why physical agents matter despite advances in large language models, outlines common failure modes, discusses key research decisions such as 2D versus 3D perception and tactile sensing, and explores the roles of imitation learning, VLA, and reinforcement learning in shaping the field.

Imitation LearningVLAVision
0 likes · 24 min read
Why Embodied Intelligence Is Exploding and What It Means for the Future
Didi Tech
Didi Tech
Jun 13, 2023 · Operations

Supply-Demand Dynamics and Regulation Techniques in Didi’s Ride-Hailing Platform

Didi balances ride‑hailing supply and demand by forecasting regional needs with time‑series and deep‑learning models, then optimally repositioning drivers through integer programming and refining policies via imitation and offline reinforcement learning, ultimately enhancing passenger experience and platform efficiency.

DidiImitation Learningforecasting
0 likes · 16 min read
Supply-Demand Dynamics and Regulation Techniques in Didi’s Ride-Hailing Platform
GuanYuan Data Tech Team
GuanYuan Data Tech Team
Sep 8, 2022 · Artificial Intelligence

How AI Reinforcement Learning Transforms Smart Replenishment in Retail

This article examines the technical challenges of intelligent replenishment—model stability, complexity, generalization, and interpretability—and explains how a few‑shot imitation learning and inverse reinforcement learning framework can overcome these issues to deliver reliable, low‑cost AI‑driven supply‑chain decisions.

AIImitation LearningModel Stability
0 likes · 22 min read
How AI Reinforcement Learning Transforms Smart Replenishment in Retail
JD Cloud Developers
JD Cloud Developers
Mar 3, 2022 · Artificial Intelligence

How JD Explore’s Silver‑Bullet‑3D Dominated the SAPIEN ManiSkill Challenge

JD Explore Research Institute’s Visual and Multimedia Lab team “Silver‑Bullet‑3D” secured top positions in the 2021 SAPIEN ManiSkill Challenge by excelling in both imitation‑learning and rule‑based tracks, showcasing cutting‑edge computer‑vision and robotic‑arm control technologies that earned them international recognition.

AI competitionImitation Learningcomputer vision
0 likes · 5 min read
How JD Explore’s Silver‑Bullet‑3D Dominated the SAPIEN ManiSkill Challenge
Tencent Cloud Developer
Tencent Cloud Developer
Mar 15, 2018 · Artificial Intelligence

Learning Long-Horizon Surgical Robot Tasks via Transition State Clustering, SWIRL, and DDCO

The article surveys three recent approaches—Transition State Clustering, Sequential Windowed Inverse Reinforcement Learning, and Deep Discovery of Continuous Options—that automatically segment long‑horizon surgical‑robot demonstrations into sub‑tasks, learn hierarchical policies from limited data, and achieve markedly higher success rates on da Vinci cutting, tension, and needle‑picking tasks.

Imitation Learninghierarchical learningreinforcement learning
0 likes · 18 min read
Learning Long-Horizon Surgical Robot Tasks via Transition State Clustering, SWIRL, and DDCO