Tags: Robotic Action Learning - Paper Library - SwiftScholar

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WHOLEBODYVLA: TOWARDS UNIFIED LATENT VLA FOR WHOLE-BODY LOCO-MANIPULATION CONTROL

Published:12/11/2025

Whole-Body Humanoid Robot ControlVision-Language-Action ModelRobotic Action LearningAction Learning from Low-Cost VideosLoco-Manipulation-Oriented Reinforcement Learning

This study presents , a unified latent visionlanguageaction framework enhancing humanoid robots' performance in locomanipulation tasks. It learns from lowcost egocentric videos and employs a tailored reinforcement learning policy, achieving a 21.3% performance b

Interactive Design of Stylized Walking Gaits for Robotic Characters

Published:7/19/2024

Robotic Action LearningLocomotion Skill Training for Humanoid RobotsDynamic Motion GenerationInteractive Robot DesignGait Generation Models

This paper presents an interactive system for creating stylized bipedal gaits for robotic characters, combining artistdirected tools with a modelbased control stack to generate physically constrained motions in real time.

Information to Users

Published:9/1/1989

Training-Free Acceleration MethodsLLM Security MechanismRobotic Action LearningMath Reasoning BenchmarksText-to-Image Generation

The paper examines concurrency control algorithms for realtime database systems, highlighting existing technical flaws and potential methods to enhance algorithm efficiency, contributing significantly to improving the reliability of realtime data processing.

Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

Published:12/19/2024

video diffusion modelsRobotic Action LearningVideo Prediction PolicyDynamic Visual RepresentationsComplex Manipulation Tasks

The Video Prediction Policy (VPP) utilizes Video Diffusion Models (VDMs) to generate visual representations that incorporate both current static and predicted dynamic information, enhancing robot action learning and achieving a 31.6% increase in success rates for complex tasks.

Riemannian Flow Matching Policy for Robot Motion Learning

Published:3/16/2024

Flow Matching PoliciesRobotic Action LearningVisuomotor PoliciesRiemannian Flow Matching PolicyGeometric-Aware Robot Control

The paper presents Riemannian Flow Matching Policies (RFMP), a model for learning robot visuomotor strategies that excels in efficient training and inference. RFMP effectively manages highdimensional, multimodal distributions and incorporates geometric awareness, outperforming e

TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning

Published:12/12/2024

Robotic Action LearningImitation LearningMobile Manipulator DesignHolonomic Mobile BaseLLM-guided motion planning

TidyBot is an opensource, lowcost holonomic mobile manipulator using powered casters for full planar freedom, enabling agile motion and simplified tasks. A phone teleoperation interface facilitates data collection for imitation learning, achieving successful household manipul

Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning

Published:5/18/2025

Vision-Language-Action ModelRobotic Action LearningLLM-guided motion planningReinforcement Learning TrainingSimulated Humanoid Control

This work introduces PDC, enabling simulated humanoids to perform multiple tasks using egocentric vision alone. Reinforcement learning yields emergent humanlike behaviors such as active search, advancing visiondriven dexterous control without privileged states.

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Robot (Imitation) Learning

Published:6/1/1999

Robotic Action LearningImitation LearningBehavioral CloningMultimodal Demonstration Dataset

This work introduces behavioral cloning for robot imitation learning, leveraging offline multimodal expert demonstrations without reward design, enabling safe, direct observationtoaction mapping and overcoming realworld reinforcement learning challenges.

Robot Learning: A Tutorial

Published:10/14/2025

Robotic Action LearningGeneralist Robot PoliciesReinforcement Learning TrainingImitation LearningLLM-guided motion planning

This tutorial presents core robot learning methods, from reinforcement learning and behavioral cloning to generalist languageconditioned policies, enabling better generalization and autonomy across diverse tasks and embodiments using datadriven approaches and lerobot examples.

Octo: An Open-Source Generalist Robot Policy

Published:5/21/2024

Generalist Robot PoliciesMulti-modal action representation and modelingTransformer architectureLarge-Scale Robot Demonstration DatasetRobotic Action Learning

Octo is an opensource transformerbased generalist robot policy pretrained on 800K trajectories, enabling fast finetuning across diverse sensors and robots, guided by language or images, demonstrating strong generalization on nine platforms.

UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers

Published:7/15/2024

Robotic Action LearningCross-Embodiment Latent Action RepresentationWhole-Body ControllerDynamic Robot ManipulationZero-Shot Cross-Embodiment Deployment

UMIonLegs integrates handheld data collection with simulationbased wholebody control, enabling zeroshot crossembodiment deployment and achieving over 70% success in diverse dynamic manipulation tasks on quadrupeds.

MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

Published:10/27/2023

Robotic Action LearningImitation LearningRobot Demonstration Dataset GenerationMulti-Task Robot LearningAutomated Data Synthesis System

MimicGen automatically generates large, diverse robot datasets from limited human demos, enabling scalable robot learning with strong performance in complex tasks, matching the effectiveness of extensive human demonstrations economically.

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