arXiv Machine Learning
Jun 24
RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes
arXiv:2606. 24403v1 Announce Type: cross Abstract: Object interaction tasks have been a focus of advances in imitation learning.
Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
arXiv:2606. 04269v1 Announce Type: cross Abstract: Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes.
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arXiv Machine Learning
Jun 16
Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos
arXiv:2602. 13197v2 Announce Type: replace-cross Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning.
arXiv Machine Learning
Aug 4
DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration
arXiv:2608. 01452v1 Announce Type: cross Abstract: Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments.
Hugging Face Trending Papers
Jun 1
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yield physically infeasible motions.
arXiv AI
Jun 2
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.
arXiv Machine Learning
Jun 10
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
arXiv:2606. 10025v1 Announce Type: cross Abstract: We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution.
arXiv AI
2d ago
Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation
arXiv:2608. 14379v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation.
Hugging Face Trending Papers
Jul 29
ContactFlow: A video action conditioning that transfers across embodiments
World models offer a promising route toward robot planning by enabling agents to imagine and verify the consequences of actions before execution. However, current video-based world models often struggle to capture the physical constraints that govern manipulation, particularly contact.
arXiv Machine Learning
Jun 16
DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.
Hugging Face Trending Papers
Jul 27
DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning
Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.
arXiv AI
Jun 17
DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation
arXiv:2605. 31286v2 Announce Type: replace-cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and household environments.
arXiv AI
Jul 7
Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
arXiv:2607. 04714v1 Announce Type: cross Abstract: Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-dimensional geometric transformations.