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
Aug 31
FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation
FlowCorrect is a modular interactive imitation learning method that allows real‑time adaptation of generative flow‑matching manipulation policies using sparse, relative human corrections. During task execution, a human provides brief corrective pose nudges through a lightweight VR interface, and FlowCorrect locally adapts the policy without retraining the backbone, maintaining performance on previously learned scenarios. Experiments on a real‑world robot across four tabletop tasks show that, with a low correction budget, FlowCorrect achieves an 80% success rate on previously failed cases while preserving performance on solved scenarios.
arXiv Computer Vision
4d ago
Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
arXiv:2609.38172v1 Announce Type: cross Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. How...
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
Aug 17
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.
arXiv AI
Sep 25
KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization
KeyGen is a framework that learns canonical 3D keypoints from point clouds to create structured, object‑centric representations for policy learning in robotic manipulation. By conditioning a visuomotor diffusion policy on these keypoints and object geometry, it predicts full manipulation trajectories that maintain geometric correspondence across different object instances. Experiments on a photorealistic simulation benchmark with three tasks show that KeyGen outperforms prior methods on both seen and unseen objects, scales with more demonstrations, remains robust to rescaling, and performs well in real‑world manipulation.