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1,589 papers
Behaviora is a preliminary conceptual architecture for representing agent and robot behavior, external and internal alike, in an addressable form and uses a primitive touching arm model to show these components and their relations.
Long-term autonomy in human-populated environments requires anticipating whether and how people will move at times a robot has not yet observed. Existing representations of pedestrian motion face a tradeoff: they either forecast future activity, reducing each location to a scalar rate, or model the full directional dis...
BEE is introduced, an intervention-adaptive framework for real-world RL on a frozen VLA that lets the policy go BEyond Expert imitation, and a Correction Model predicts how a human would correct a given VLA proposal and how consistent the correction is along each action dimension.
SGCA-Net is proposed, a spinning radar place recognition framework that combines rotation-robust feature extraction with Spatially Gated Correlation Aggregation (SGCA) and consistently outperforms SOTA methods across urban, campus, and open-road environments, while remaining robust to substantial heading variation.
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Results show that GFM-derived geometry can provide a persistent structural prior for VPR when visual appearance becomes unreliable, with the largest gains under reverse traversal and long-term appearance variation.
It is found that fine-tuned imitation learning policies converge to similar action predictions across priors, despite their fine-tuned encoder embeddings diverging substantially from the pretrained embeddings and each other.
Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversability depends on both the environment and the embodiment's dynamics. Neither of these two variables can be hand-labeled at scale. Thus, traversability has to be learned by th...
SlackDrive is proposed, a pre-inference compute allocator that reuses realized latency to select the compute budget of each control step before model execution, complementing existing profiling and resource scheduling while preserving the driving backbone and its compute actuator.
Talk2Escape is introduced, a proactive and model-agnostic dialogue intervention framework that reframes navigation as a closed-loop interactive process and proves that proactive dialogue drastically improves navigation robustness in physical environments.
Electrovibration-based tactile feedback is demonstrated to be a viable and effective modality for robot teleoperation, improving operator responsiveness and sense of presence in contact-rich manipulation tasks, with direct applicability to safety-critical domains such as nuclear maintenance.
This work proposes DreamAvoid, a critical-phase test-time dreaming framework that enables VLA models to anticipate and avoid failures, and introduces an autonomous boundary learning paradigm to refine the system's understanding of the subtle boundary between success and failure.
Across towel folding, book insertion, and Hanoi ring placement, LIFT learns faster and reaches higher performance than vision-only post-training, while ablations show that reactive force memory and online corrective data are both important for robust contact-rich manipulation.
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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