--- source: arxiv url: http://arxiv.org/abs/2604.15671v1 published_at: '2026-04-17T03:49:41' authors: - Xu Huang - Weixin Mao - Yinhao Li - Hua Chen - Jiabao Zhao topics: - vision-language-action - robot-memory - long-horizon-planning - chemical-lab-automation - closed-loop-control relevance_score: 0.88 run_id: materialize-outputs language_code: en --- # Long-Term Memory for VLA-based Agents in Open-World Task Execution ## Summary ChemBot targets long-horizon chemical lab execution with a VLA agent that can plan, act, track progress, and reuse past successful trajectories. The paper’s main claim is that adding dual-layer memory, closed-loop subtask planning, and a progress-aware VLA improves decomposition quality and real-world task success over VLA baselines. ## Problem - VLA systems can map language and vision to robot actions, but they often struggle with long, state-dependent lab protocols where errors compound across many steps. - Existing systems usually separate planning from execution and do not keep a persistent memory of successful runs, so the robot repeats trial-and-error instead of reusing what worked. - This matters in chemical labs because tasks are multi-stage, safety-sensitive, and require precise switching between subtasks based on the current physical state. ## Approach - The system, called **ChemBot**, uses a two-level design: an LLM-based agent plans the task as atomic subtasks, and a VLA policy called **Skill-VLA** executes each subtask on the robot. - A **Scene Describer** converts the current bench image into a structured dashboard with objects, states, affordances, and spatial relations, so planning uses grounded scene information instead of raw images alone. - Planning runs in a closed loop with a **Subtask Reasoner** that proposes one atomic step at a time and a **Reflector** that checks feasibility, redundancy, and sequence validity before adding the step to the queue. - The memory system has **short-term memory** for current scene state, tool outputs, and task progress, plus **long-term episodic memory** that stores prior successful trajectories for semantic retrieval in later experiments. - **Skill-VLA** builds on GR00T and adds a **progress head** that predicts a scalar subtask completion value in [0,1]. When progress crosses a threshold, the agent ends the current skill and starts the next one. The paper also adds asynchronous future-state inference to reduce action discontinuities. ## Results - For chemical task decomposition, among tested base VLMs, **Qwen3-VL-Plus** had the best text/structure scores with **Edit Distance 0.733**, **BERTScore F1 0.744**, **ROUGE-1 0.542**, **ROUGE-2 0.366**, and **ROUGE-L 0.471**. **Qwen3-VL-Flash** was faster at **98.8 s** total and **16 s/step** versus **298.8 s** and **42 s/step** for Qwen3-VL-Plus. - In ChemBot ablations with Qwen3-VL-Flash, the **full model** reached **Edit Distance 0.766**, **BERTScore F1 0.694**, **ROUGE-L 0.324**, **98.8 s** runtime, and **22,401 tokens**. Removing the **Scene Describer** hurt decomposition quality most, raising Edit Distance to **0.915** and dropping BERTScore F1 to **0.556**. - Removing the **Subtask Chain** caused the largest structural collapse in ablations, with **Edit Distance 1.195** versus **0.766** for the full model. Removing the **Reflector** gave **Edit Distance 0.881**. Removing **Memory** increased token length from **22,401** to **28,064**, suggesting memory mainly reduces context load while keeping quality close to the full model. - The robot dataset used to train Skill-VLA contains **5,459 expert trajectories**, averaging **872.2 frames**, with **51,580 atomic action segments** across **12** categories of chemical procedures. - Real-world evaluation used a **UR3** robot, **16 trials per task**, and three multi-step experiments: **Precipitation (11 steps)**, **Heat and Dissolution (7 steps)**, and **Neutralization (6 steps)**. The paper states that Skill-VLA with subtask-level training outperformed full-trajectory baselines **\(\pi_{0.5}\)** and **GR00T** on all three tasks. - The excerpt does **not provide the exact success-rate percentages** from Fig. 8, so the strongest supported claim is qualitative: the full ChemBot strategy achieved higher real-world success rates, safety, and precision than full-trajectory VLA baselines, and the authors attribute the gain to subtask decomposition plus progress-based skill switching. ## Link - [http://arxiv.org/abs/2604.15671v1](http://arxiv.org/abs/2604.15671v1)