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1,589 papers
Results show that learned predictions of future safety cost can provide an effective signal for allocating training experience toward rare failure modes and improving locomotion reliability.
Experiments show that FACT improves geometric reconstruction over baselines, enables more reliable interaction with simpler collision proxies, and better reproduces held-out physical responses than direct parameter inference.
This work presents AeroManip-VLA, a scalable benchmark for aerial VLA data generation and policy evaluation, and evaluates a range of imitation learning and VLA baselines across different task settings, revealing their performance characteristics and failure modes.
This work forms a KL-regularized objective whose optimum induces a Gibbs policy over final actions under a Gaussian-smoothed behavior prior, and introduces Proposal-Conditioned Refinement Flows (PReFlow), a policy extraction method combining critic-based proposal selection with a conditional refinement flow.
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Spotter is proposed, which reverses the roles: the embodied model leads and executes continuously, while the VLM runs in parallel, monitors through a lightweight local screener, intervenes only when an error is detected, reflects on and corrects it, and returns control.
This work studies what determines whether predictive supervision improves the visual representation used by a VLA policy, and finds that different prediction interfaces produce markedly different forecasts and visual representations, including in the spatial, dynamics, and action information that transfers beyond famil...
These results support a state-based view of robot memory: effective memory for control is not simply retained visual history, but compact task-relevant state derived from the interaction history.
A three-stage reinforced fine-tuning (RFT) pipeline for multi-agent VLAs is presented, which freezes the VLA and performs RL in its latent noise space and finds existing online RL for VLAs are less effective for hard multi-agent tasks, which are attributed to noisy co-exploration and unstable updates.
Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previous scene. Many simulation robustness benchmarks fix external conditions at reset, leaving this temporal challenge underexamined. We introdu...
STAIRCASE POLICY is introduced, a streaming inference and training framework that turns a flow-matching VLA into a JEPA-style WAM and partitions a large action chunk into sub-chunks at staggered denoising stages, enabling long-horizon execution without repeated full policy inference.
FineART is presented, a densely annotated bimanual manipulation dataset comprising 40,543 episodes and 533,913 subtasks across 151 tasks and FineART-VLA is introduced, a vision-language-action policy that predicts its own next subtask to guide its actions.
This work introduces Aligned Transport of Latent Structure (ATLAS), a training objective that explicitly preserves relational geometry while calibrating the global latent distribution and uses Wasserstein embedding matching to calibrate its marginal through one-dimensional Wasserstein-2 transport.
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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