arXiv:2610.18874v1 Announce Type: cross Abstract: Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-actio...
arXiv:2610.01939v1 Announce Type: new Abstract: Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant obser...
arXiv:2609.36471v1 Announce Type: cross Abstract: World-Action Models (WAMs) improve robotic manipulation by conditioning action generation on predicted future observations, but future prediction add...
arXiv:2609.08638v1 Announce Type: cross Abstract: An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-A...
The paper introduces a ROS-Agent architecture that enhances task reliability and execution efficiency for open‑source LLM‑powered robotic agents. It adds a MetaTool that forces the LLM to produce a structured pseudo‑code plan before any action, storing this plan in a scratchpad to separate planning from execution. Experiments on a custom mobile robot show up to ~24% improvement in complex task completion and contextual consistency compared to the baseline.
arXiv:2605.13119v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burde...
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
The paper introduces 2AM, a system that keeps task memory solely within a multimodal Agent while using a single RGB‑based, stateless Action Model to execute motions. By compiling interaction history into subtask language and optional 2D grasp/place/move hints, the Agent steers the Action Model, which is trained to tolerate imperfect guidance through dropout, noise, and jitter. On the LIBERO‑Mem benchmark, 2AM achieves 76.3% average completion without depth, geometry, or planners, vastly outperforming the best baseline.
The paper introduces Reasoning and Flow Memory (FLOWMEM), a vision‑language‑action model that reuses successful latent reasoning steps instead of discarding them after each policy query. FLOWMEM dynamically retrieves and recomposes compatible latent fragments to form a reasoning route that aligns with the temporal structure of prior successful computations, then refines this route with current visual and proprioceptive data before generating actions. Experiments on RoboMME and LIBERO‑Plus show that FLOWMEM improves success rates by 1.7 and 4.1 percentage points over memory‑free policies, demonstrating the benefit of reusing latent computation for closed‑loop control.
Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol, issuing one primitive action per LLM round. While this enables frequent replanning, it is...
The paper introduces Latent Transition Memory (LTM), a method that stores interaction history as compact latent transitions between consecutive observations for vision‑language‑action models. Implemented as LTM‑π on the π₀.₅ architecture, it uses a frozen latent action encoder, a lightweight adapter, and causal KV caching to enable incremental inference without external reasoning models or memory‑specific annotations. Experiments on RoboMME and LIBERO‑Mem show that LTM‑π achieves state‑of‑the‑art success rates (58.54% and 80.5% respectively), and scaling the dataset or applying a 200‑token memory budget still yields high performance, demonstrating the method’s scalability and efficiency in real‑world robotic tasks.
The paper introduces Divide-and-Remember (D&R), a recursive memory method for vision-language-action (VLA) policies that optimises memory by maximizing the conditional mutual information between actions and memory given observations. D&R recursively divides the full history into top‑K selections over 2K tokens, using a shared lightweight selector across all recursion blocks to handle unbounded histories efficiently. Evaluated on the RoboMME benchmark of 16 long‑horizon manipulation tasks, D&R achieves state‑of‑the‑art success rates with consistent gains across all suites while using only 64 tokens, and similar improvements are observed in real‑robot experiments.