arXiv:2607. 05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations.
arXiv:2609.25627v1 Announce Type: cross Abstract: General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate prec...
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
arXiv:2609.39235v1 Announce Type: cross Abstract: World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet exis...
arXiv:2610.03476v1 Announce Type: cross Abstract: Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and...
arXiv:2605. 30226v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for grounding visual-language understanding into real-world robotic manipulation.
arXiv:2610.36013v1 Announce Type: new Abstract: General-purpose embodied intelligence requires robots to coordinate diverse skills over long horizons, track task progress, and recover from failures....
arXiv:2604. 21391v2 Announce Type: replace-cross Abstract: Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal scale mismatch between cognition and action.
arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.
CoDA is a framework that lets a simulation data engine and an action policy evolve together using a shared memory of environment construction, policy failures, and learning progress. The memory stores validated diagnoses from a vision‑language critic, which are turned into generation programs that recreate challenging scenarios and training programs that help the policy improve. Using this approach, CoDA boosts out‑of‑distribution success on several manipulation tasks, raising success rates from 5.3 % to 27.6 % and achieving up to 84 % success on LIBERO‑Pro under perturbations.
arXiv:2607. 02431v1 Announce Type: cross Abstract: Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.
RoboGene is an agentic framework designed to generate diverse and informative manipulation tasks for single-arm, dual-arm, and mobile robots. It employs diversity-driven sampling, agentic self-reflection for task quality assessment, and human-verified feedback memory to validate and refine tasks. The framework produced 18,000 real-world trajectories and introduced new metrics for task feasibility and diversity, outperforming models like GPT‑4o and Gemini 2.5 Pro in training VLA models that achieve higher success rates and stronger generalization.