WAM-OPD, a deployment-consistent post-training recipe for a video-first WAM, is introduced and it is suggested that dense teacher supervision on student-induced histories is a promising post-training interface for video-first WAMs.
Liu Yang, Zhuang Jiang, Chenchao Sheng et al.· 2 citations
GlanceWAM is introduced, which decouples imagination from control within a single video DiT: an asynchronous proposer glances ahead on a slow clock to imagine a single lookahead frame seconds into the future in the background, while an action head decodes action chunks at control rate purely in latent space without blo...
Lin-Han Wang, Zi-Jian An, Mingyuan Zhang et al.· 3 citations
The proposed hierarchical long-horizon VLA architecture with an explicit language-memory module improves the success rate and robustness of VLA models on complex long-horizon tasks while providing an interpretable semantic account of the decision process.
This work proposes World Action Planner, a robot planning system that leverages the reasoning capabilities of Vision-Language Models (VLMs) and the physical grounding of a multi-task pose-image conditioned world model, significantly outperforming state-of-the-art end-to-end policy models such as VLAs and WAMs.
EgoWAM is introduced, a controlled human-robot co-training framework that fixes the policy backbone, action head, and data mixture while varying only the world prediction target, comparing Pixel, DINO, and 3D motion flow.
Baoyu Li, Xi Yin, Mengying Lin et al.· 5 citations
LM-X is introduced, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence and shows that explicit multi-timescale predictive state can strengthen control while exposing interpretable internal estimates.
Jin Lou, Zhi Jing, Xu-Peng Wang et al.· 0 citations
This work examines whether a robot's action representations preserve the semantic structure captured by pretrained encoders and introduces a plug-and-play method that anchors action representations to a semantic manifold while decomposing representations into a shared semantic channel and a private channel, all discard...
Yuan Xu, Youheng Shi, Chengyang Li et al.· arXiv.org· 0 citations
This work presents FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision and shows that FleetScape supports situational awareness through layered spatial repres...
Peisen Xu, Jérémie Garcia, Peter Cleveland et al.· 0 citations
It is found that the MPA significantly influenced the PMPA and perceived virtue scores, and the best-fitting model to describe the relationship between PMPA and PVS was cubic, which is symmetrical in nature.
Min-Ying Wang, Christoph Bartneck, Michael-John Turp et al.· ACM Transactions on Human-Ro...· 0 citations
The results clearly demonstrate that FlowCorrect learns from very few demonstrations and enables fast, sample-efficient, incremental, human-in-the-loop corrections of generative visuomotor policies at deployment time in real-world robotics.
Edgar Welte, Yitian Shi, R. Wolf et al.· arXiv.org· 4 citations
This system discovers causally linked action pairs directly from the training data, where one action produces exactly the condition that the next one requires, and turns each pair into a new operator, and not only accelerates planning but, in certain domains, determines solvability in practice.
Meta-Ctrl is proposed, a constrained-decoding framework that guarantees the encoded constraints while preserving the base LM's plan quality, and is demonstrated on a real tabletop robot, where every generated plan satisfies its preconditions and goals by construction.
Gwen Yidou-Weng, Edward Sun, Tian-Yi Ma et al.· 0 citations
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.