World-Action Models (WAMs) jointly predict future world states and robot actions for diverse manipulation tasks, but they can suffer from Stage Confusion—generating plausible yet misdirected rollouts that drift to the wrong task stage. TaskWAM introduces a closed-loop, stage-aware framework using Mode Tokens and State Tokens to represent fine-grained task requirements and execution progress, steering generation toward the intended future. The authors also present StageBench, a multi-stage manipulation benchmark, and show through extensive simulation and real-world experiments that TaskWAM reduces confusion, matches state-of-the-art performance, and transfers effectively across various robot embodiments.
Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this promise requires precise action-conditioned transitions rather than merely plausible outputs.
arXiv:2608. 09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation.
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.
Hierarchical World Models with Implicit Dynamics (ID‑WM) introduces a two‑level approach that separates state‑space reachability from local control. A high‑level model samples feasible transitions across the state manifold, while a low‑level world model acts as an inverse dynamics solver to translate these transitions into precise actions. This design enables planners to identify reachable subgoals without being constrained by low‑level execution details, producing novel long‑horizon plans that outperform state‑of‑the‑art policy and diffusion‑based planners by 38.5% and single‑level MPC by 135% in complex, unseen configurations.
CogWAM is a cognition‑guided world‑action model that links task reasoning with action learning via a persistent Semantic State, which records completed task events and the active subtask. The model updates this state only on semantic transitions, enabling task‑level context to persist across multiple action chunks. During inference, CogWAM removes the future‑prediction branch and directly generates actions from observations and the maintained state, achieving state‑of‑the‑art performance on RoboDojo and BiCoord and demonstrating efficient closed‑loop dual‑arm manipulation in real‑world experiments.
arXiv:2607. 15641v1 Announce Type: cross Abstract: Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.
arXiv:2610.10409v1 Announce Type: cross Abstract: General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities car...
arXiv:2606. 26423v1 Announce Type: cross Abstract: Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-box generalization to new tasks.
arXiv:2608.22067v1 Announce Type: cross Abstract: World-Action Models (WAMs) build robot control on video-generation backbones, which jointly predict dense future visual trajectories and robot action...
AffordSim is a scalable data generator and benchmark that integrates open‑vocabulary 3D affordance prediction into simulation‑based trajectory generation for robotic manipulation. Given a natural‑language task description, it synthesizes task‑relevant scenes, queries affordances, grounds them on object surfaces, samples region‑conditioned grasps, and selects executable candidates with motion planning, while randomizing pose, texture, lighting, noise, and backgrounds for sim‑to‑real transfer. The benchmark includes 50 tasks, five robot embodiments, and over 500 objects, achieving 93% of the success rate of manual contact annotations on affordance‑critical tasks and 89% on hard composite tasks, with policies trained on AffordSim transferring zero‑shot to a real Franka FR3 with 24% average success.
DELE-w0.5 is a robotic manipulation framework that predicts future latent states instead of generating full video sequences, thereby inferring robot actions directly from these compact representations. By focusing on physical state changes rather than visual transitions, it reduces model complexity and inference latency. In 480 real‑robot trials across four long‑horizon tasks, DELE‑w0.5 achieved 62.5 % overall task success and 81.3 % macro ordered‑stage progress, outperforming the strongest baseline by 47.5 and 30.7 percentage points.
arXiv:2606. 13053v1 Announce Type: cross Abstract: Pretrained-feature world models provide a useful substrate for robot imagination, but visual or latent prediction alone does not determine whether an imagined future satisfies task-relevant events.