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1,589 papers
AD-WM, an action-discriminative joint-embedding world model for counterfactual MPC, is introduced, which combines residual latent dynamics with predictor-level action-recovery regularization, using inverse dynamics and a normalized recovery objective motivated by conditional mutual information.
ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement, and ART reduces the complexity of the action solution space through tool-use, which improves generalizability across different tasks but also reduces...
EA-WM is presented, an Event-Aware Generative World Model that effectively closes the loop between kinematic control and visual perception, and introduces event-aware bidirectional fusion blocks that modulate cross-branch attention, capturing object state changes and interaction dynamics.
This work introduces MessyKitchens, a new dataset with real-world scenes featuring cluttered environments and providing high-fidelity object-level ground truth in terms of 3D object shapes, poses and accurate object contacts, and builds on the recent SAM 3D approach for single-object reconstruction and extends it with...
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A decentralized Perception-Action-Communication system for multi-robot teams that enables them to collaborate in large scale, outdoor environments and upholds crucial properties for field-deployable robot collectives: scalability, resiliency and repeatability.
Language-grounded robot group joining is formulated: given an observation and a natural-language description of a target group, the robot identifies the relevant group members and predicts socially compliant joining poses, and this method achieves competitive grounding accuracy with sub-second inference and outperforms...
Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulation depends on most: motion. The pretrained flow never moves the manipulated object; retraining it with latent-only losses only trades stillness for teleport-like motion....
Findings support MemBodied as a practical alternative to expanding the policy context for history-dependent manipulation, a fixed-size episodic memory with two complementary components: an associative state that records interactions across policy calls and an episode anchor that preserves a compact representation of th...
This work proposes COMPASS, a scalable, decentralized multi-robot architecture for controlling large collectives of agentic robots with reasoning space feedback control, and shows that reasoning feedback works best when composed with a compact learned token.
This study presents a centralized safety aware M2M framework for cooperative goal-directed navigation in a heterogeneous mobile robot system composed of a vision-capable robot and a cameraless robotic vehicle that can approve safe motion, trigger conservative replanning or holding behavior, and preserve a strict separa...
InfiNoVA is introduced, a data-augmentation framework that converts synchronized multi-camera demonstrations into a dense distribution of geometrically consistent training views that improves frame-level fidelity and temporal consistency while reducing task-critical hallucinations observed in generative novel-view synt...
This work introduces InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences.
A Blog post by Ai2 on Hugging Face
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.
Discover Gemini 4 Argon for robotics: leading the ERQA benchmark with physical reasoning, zero-shot control, Real2Sim, and agentic workflows.
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.
A Blog post by Amazon on Hugging Face
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