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Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
arXiv:2606.08107v1 Announce Type: new Abstract: Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulation. A promising path forward is to leverage egocentric human data, which can be collected more easily, with greater breadth, and at a larger scale.
Nvidia CEO says company is working with LG on humanoid robots and data centers
Nvidia CEO says company is working with LG on humanoid robots and data centers SEOUL, June 8 : Nvidia CEO Jensen Huang said on Monday that it is partnering with South Korea's tech conglomerate LG Group on humanoid robots and data centers. "We are working with them in motor technology as well as mechanical systems so that we can bring together humanoid robotics and the future of robotics," he told reporters after a meeting with LG Group Chairman Koo Kwang-mo in Seoul. "We're also working with...
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
arXiv:2606.02577v1 Announce Type: new Abstract: 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. We present a generalizable...
RoboCade: Gamifying Robot Data Collection
Announce Type: replace Abstract: Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies. However, collecting demonstration datasets is costly: it often requires access to robots and needs sustained effort in a tedious, long process. These factors limit the scale of data available for training policies.
LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs
arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of human demonstrations (as few as one) into a large robot dataset for imitation learning. Our approach decomposes demonstration generation into two steps: (1) offline demonstration annotation that extracts keyframes, salient objects, and pose-object relations; and (2) online keypose retargeting...
Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation
arXiv:2606.01047v1 Announce Type: new Abstract: Robotic manipulation requires the effective integration of heterogeneous inputs, including visual observations, language instructions, and trajectory representations, to generate accurate actions. Existing transformer-based policies typically process these heterogeneous modalities within a shared parameter space, which often leads to modality interference and inefficient representation learning, especially in data-scarce scenarios. While...
RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning
arXiv:2605.30957v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robot control. However, their performance remains fundamentally constrained by the availability of high-quality robot trajectory data. In current robot learning practice, such data are primarily collected through human teleoperation, which is labor-intensive, costly, and difficult to scale.
Is Diversity All You Need for Scalable Robotic Manipulation?
Announce Type: replace Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional...
Targeting World Models to Compromise Robot Learning Pipelines
arXiv:2606.09499v1 Announce Type: new Abstract: World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world environments, with many works proposing their integration into the robot learning pipeline. While highly practical, in this work we demonstrate that world models introduce a uniquely stealthy and effective data poisoning entry point into the robot learning supply chain...
ActiveMimic: Egocentric Video Pretraining with Active Perception
Announce Type: new Abstract: Egocentric human video offers a scalable alternative to robot data for pretraining, yet models pretrained on such video consistently underperform those pretrained on robot data. We attribute this gap to a missing signal, the active perception behavior in egocentric videos, where humans continuously reposition their viewpoint during manipulation, inducing camera motion that standard pipelines treat as noise. To address this, we present ActiveMimic, a pretraining...