Copertina

Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Oh, Hanbit, Murooka, Masaki, Motoda, Tomohiro, Nakajo, Ryoichi, Domae, Yukiyasu
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Robotics Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
di: Murooka, Masaki, et al.
Pubblicazione: (2025)

Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers
di: Motoda, Tomohiro, et al.
Pubblicazione: (2025)

Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation
di: Muttaqien, Muhammad A., et al.
Pubblicazione: (2025)

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
di: Muttaqien, Muhammad A., et al.
Pubblicazione: (2025)

Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks
di: Oh, Hanbit, et al.
Pubblicazione: (2024)

SuctionPrompt: Visual-assisted Robotic Picking with a Suction Cup Using Vision-Language Models and Facile Hardware Design
di: Motoda, Tomohiro, et al.
Pubblicazione: (2024)

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
di: Oh, Hanbit, et al.
Pubblicazione: (2025)

Visual Imitation Learning of Non-Prehensile Manipulation Tasks with Dynamics-Supervised Models
di: Mustafa, Abdullah, et al.
Pubblicazione: (2024)

A Flexible Field-Based Policy Learning Framework for Diverse Robotic Systems and Sensors
di: Carreon, Jose Gustavo Buenaventura, et al.
Pubblicazione: (2025)

Motion Priority Optimization Framework towards Automated and Teleoperated Robot Cooperation in Industrial Recovery Scenarios
di: Itadera, Shunki, et al.
Pubblicazione: (2023)

Imitation Learning with Precisely Labeled Human Demonstrations
di: Song, Yilong
Pubblicazione: (2025)

Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots
di: Shaklab, Eyad, et al.
Pubblicazione: (2023)

Centroidal Trajectory Generation and Stabilization based on Preview Control for Humanoid Multi-contact Motion
di: Murooka, Masaki, et al.
Pubblicazione: (2025)

Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models
di: Lucny, Andrej, et al.
Pubblicazione: (2023)

Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards
di: Kwon, Hyeokjin, et al.
Pubblicazione: (2024)

TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control
di: Zhuang, Zifeng, et al.
Pubblicazione: (2025)

Replaceable Bit-based Gripper for Picking Cluttered Food Items
di: Kumar, Prashant, et al.
Pubblicazione: (2026)

SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation
di: Bahety, Arpit, et al.
Pubblicazione: (2025)

Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
di: Yamane, Koki, et al.
Pubblicazione: (2026)

Augmented Reality Demonstrations for Scalable Robot Imitation Learning
di: Yang, Yue, et al.
Pubblicazione: (2024)

Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
di: Wu, Kun, et al.
Pubblicazione: (2024)

ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning
di: Kim, Byung-ju, et al.
Pubblicazione: (2025)

DIRIGENt: End-To-End Robotic Imitation of Human Demonstrations Based on a Diffusion Model
di: Spisak, Josua, et al.
Pubblicazione: (2025)

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
di: Arachchige, Nadun Ranawaka, et al.
Pubblicazione: (2025)

Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
di: Kuhar, Sachit, et al.
Pubblicazione: (2023)

Self-evolved Imitation Learning in Simulated World
di: Ye, Yifan, et al.
Pubblicazione: (2025)

See, Learn, Assist: Safe and Self-Paced Robotic Rehabilitation via Video-Based Learning from Demonstration
di: Alabbas, Ali, et al.
Pubblicazione: (2026)

Efficient Imitation Without Demonstrations via Value-Penalized Auxiliary Control from Examples
di: Ablett, Trevor, et al.
Pubblicazione: (2024)

Autonomous Soft Robotic Guidewire Navigation via Imitation Learning
di: Barnes, Noah, et al.
Pubblicazione: (2025)

MILES: Making Imitation Learning Easy with Self-Supervision
di: Papagiannis, Georgios, et al.
Pubblicazione: (2024)

End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data
di: Park, Jin Bok, et al.
Pubblicazione: (2023)

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations
di: Patel, Shivansh, et al.
Pubblicazione: (2025)

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN
di: Cibula, Miroslav, et al.
Pubblicazione: (2025)

ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
di: Chen, Sirui, et al.
Pubblicazione: (2024)

Optimization-based Posture Generation for Whole-body Contact Motion by Contact Point Search on the Body Surface
di: Murooka, Masaki, et al.
Pubblicazione: (2025)

TACT: Humanoid Whole-body Contact Manipulation through Deep Imitation Learning with Tactile Modality
di: Murooka, Masaki, et al.
Pubblicazione: (2025)

Sample-Efficient Reinforcement Learning with Symmetry-Guided Demonstrations for Robotic Manipulation
di: Enayati, Amir M. Soufi, et al.
Pubblicazione: (2023)

Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
di: Murooka, Masaki, et al.
Pubblicazione: (2025)

IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning
di: Hoque, Ryan, et al.
Pubblicazione: (2024)

ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning
di: Yang, Yue, et al.
Pubblicazione: (2024)