RIO introduces a lightweight open-source framework that abstracts real-time robot I/O to support easy switching between embodiments and platforms for collecting data and deploying VLAs.
Rlds: an ecosystem to generate, share and use datasets in reinforcement learning
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
A typed-contract harness with containerized 'chambers' and robotics-specific agent skills lets a coding LLM turn a single natural-language prompt into working reproduction, evaluation, and deployment workflows for robot learning.
ATLAS is a multi-modal annotation tool for robotic action segmentation that natively supports ROS bags and RLDS formats, reducing per-action annotation time by at least 6% versus ELAN and cutting boundary error fivefold when time-series data is included.
ABot-M0 unifies heterogeneous robot data into a 6-million-trajectory dataset and introduces Action Manifold Learning to predict stable actions on a low-dimensional manifold using a DiT backbone.
Current agentic RL systems lack three key components needed for self-evolving agents at scale, requiring new co-designed architectures such as AReaL2.0 to enable policy updates from deployed workloads.
Real-robot trials with OpenVLA on a UR5e arm show consistent offline-to-closed-loop gaps driven by action semantics, coordinate conventions, temporal alignment, image preprocessing, and dataset quality rather than model capacity.
citing papers explorer
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RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning
RIO introduces a lightweight open-source framework that abstracts real-time robot I/O to support easy switching between embodiments and platforms for collecting data and deploying VLAs.
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Nautilus: From One Prompt to Plug-and-Play Robot Learning
A typed-contract harness with containerized 'chambers' and robotics-specific agent skills lets a coding LLM turn a single natural-language prompt into working reproduction, evaluation, and deployment workflows for robot learning.
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ATLAS: An Annotation Tool for Long-horizon Robotic Action Segmentation
ATLAS is a multi-modal annotation tool for robotic action segmentation that natively supports ROS bags and RLDS formats, reducing per-action annotation time by at least 6% versus ELAN and cutting boundary error fivefold when time-series data is included.
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ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
ABot-M0 unifies heterogeneous robot data into a 6-million-trajectory dataset and introduces Action Manifold Learning to predict stable actions on a low-dimensional manifold using a DiT backbone.
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Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents
Current agentic RL systems lack three key components needed for self-evolving agents at scale, requiring new co-designed architectures such as AReaL2.0 to enable policy updates from deployed workloads.
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Vision-Language-Action Models: Experimental Insights from a Real-World UR5 Platform
Real-robot trials with OpenVLA on a UR5e arm show consistent offline-to-closed-loop gaps driven by action semantics, coordinate conventions, temporal alignment, image preprocessing, and dataset quality rather than model capacity.