WAM-OPD introduces a method for post‑training improvement of World Action Models (WAMs) by collecting student rollout histories and querying a stronger teacher for paired video and action targets. The student learns from both modalities while preserving its one‑step generation capability at deployment. Experiments on 12 RoboTwin 2.0 tasks and four real‑robot tasks show success rates rising from 33.8% to 65.7% and from 51.4% to 64.6%, respectively, with joint video‑action supervision outperforming single‑modality approaches.
arXiv:2608.22364v1 Announce Type: new Abstract: World action models (WAMs) couple visual future prediction with robot action generation, but accelerated students can lose task capabilities during dis...
arXiv:2609.38057v1 Announce Type: new Abstract: Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action m...
arXiv:2610.03391v1 Announce Type: new Abstract: World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotat...
MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.
Zero-WAM introduces a causal video-action model that enables robots to perform unseen manipulation tasks by following in-context human video guidance. The authors create HumanGen, a dataset of 74.2K human-robot ICL pairs across 8.6K tasks, and propose an in-context future chunk prediction objective to prevent shortcut learning. In simulation, Zero-WAM attains a 47.0% success rate on seven unseen tasks, outperforming the best video-action baseline by 29.5 percentage points, and demonstrates real‑world generalization to complex, long‑horizon, and fine‑grained tasks.
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task ca...
arXiv:2607. 04265v1 Announce Type: cross Abstract: World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion.
DeltaWAM introduces a new approach to world-action models (WAMs) for bimanual manipulation by jointly predicting visual deltas and actions instead of dense future frames, thereby reducing redundant modeling of unchanged content and mitigating nuisance appearance variations. The method employs three architectures with varying representation and computation sharing, and incorporates Streaming Delta Memory (SDM) to update cached anchor context using compact observed deltas, which cuts heavy video-expert processing. Experiments on RoboTwin show that DeltaWAM with SDM raises average success rates from 81.3% to 85.4% in clean settings and from 75.8% to 83.9% under visual randomization, while also reducing training FLOPs by up to 23.77% and inference latency by 36.57%. whyItMatters":"DeltaWAM improves both performance and computational efficiency for bimanual manipulation tasks by focusing on visual deltas and efficient memory updates, as demonstrated by higher success rates and lower FLOPs on RoboTwin."
HIL-UMI is a policy-guided Universal Manipulation Interface that enables robot‑free, human‑in‑the‑loop post‑training of vision‑language‑action models. By querying the current policy during handheld demonstrations and using an Energy Score to detect out‑of‑distribution states, it selectively collects new data and refines a progress‑based advantage estimator. The updated estimator then drives advantage‑conditioned behavioral cloning, improving performance on long‑horizon and precise manipulation tasks while reducing per‑frame collection time compared to HG‑DAgger.
arXiv:2607. 06988v1 Announce Type: cross Abstract: Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot demonstrations, task-specific fine-tuning, or long-context conditioning.
arXiv:2609.38059v1 Announce Type: cross Abstract: Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a sca...
arXiv:2606. 09811v1 Announce Type: cross Abstract: World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning.