AtomBridge: Plugin for Long-Horizon Tasks in Robotic Labs
AtomBridge, a novel AI plugin, seeks to enhance vision-language-action (VLA) models' ability to conduct long-horizon scientific experiments by tackling the challenges posed by robot-state mismatches in skill chaining. This plugin integrates with a pre-tuned VLA policy during inference, maintaining its weights. It employs LLM-based trajectory generation at each task transition to connect the end state of one skill with the starting state of the subsequent task. The study, which can be found on arXiv (2602.09430), addresses the shortcomings of current VLA models in executing composed tasks through the reordering and combination of known atomic actions. Announced as a replace-cross on arXiv, the authors present AtomBridge as a means to improve VLA models' performance in long-horizon tasks, fostering advancements in autonomous scientific discovery.
Key facts
- AtomBridge is an agentic VLA inference plugin for long-horizon tasks in scientific experiments.
- It addresses the skill-chaining gap caused by robot-state mismatch.
- The plugin attaches at inference time to a VLA policy fine-tuned on atomic tasks.
- It keeps the VLA policy weights fixed.
- It uses LLM-based trajectory generation at task boundaries.
- The paper is available on arXiv with ID 2602.09430.
- The announcement type is replace-cross.
- Robotic laboratories are critical for autonomous scientific discovery.
Entities
Institutions
- arXiv