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1,589 papers
A metric interaction framework that models object-level and scene-level interactions in physical Cartesian space at a shared metric scale and improves diverse VLA and WAM baselines with a small number of additional parameters and training steps is introduced.
This work instantiates Real-Time EXPO-FT, an RL framework for finetuning real-time VLA policies that meets the real-time control requirements of dynamic real-world manipulation, demonstrating rapid, sample-efficient adaptation to challenging real-world dynamics.
The results show that replay retention depends on change magnitude and on how the dynamics evolve, and an estimator built from interaction data can still provide the quantities needed to choose a replay strategy after permanent changes.
For the datasets and models considered, pixel-level image-fidelity metrics alone may not consistently capture the task-relevant realism of synthetic sonar observations and motivate the use of task-aware evaluation for synthetic sensor data intended for robotic perception.
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Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to every network region as if every region requires equal adjustment. This paper tests that assumption on five architecturally diverse VLAs (OpenVLA-OFT, $\pi_0$, SmolVLA, DTP...
FedGuide is proposed, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning and develops a Distribution Correction Estimation value baseline to provide low-variance, return-aware guidance for local policy improvement.
These results give expected policy-loss consistency for the fixed-horizon generative-reset approximate-ERM procedure with exact action scores and provide an explicit residual-decay criterion for FIFO/interleaved SGD.
CloudEdgeVLA is introduced, a cloud-edge policy that treats temporal misalignment as a representation-learning problem and offers a practical route to scalable VLA deployment in which cloud models can grow while edge computation remains lightweight and responsive.
EfficientTDMPC is introduced, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms that is the new state of the art in sample efficiency on HumanoidBench and the DeepMind Control Suite.
PACT-WAM is introduced, a world-action model that jointly generates a 16-step action trajectory and its temporally corresponding visual forecast through conditional flow sampling, andlations show that hierarchical history allocation and joint action-visual generation improve control success.
Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant computations, large memory footprints, and complex integration challenges. In response, this work presents Visual Perception Engine (VPEngine), a modular framework designed to e...
A novel vision-language model (VLM)-based navigation framework that addresses challenges by adaptively retrieving task-relevant cues from a global memory module and integrating them with the agent's egocentric observations and enhances spatial reasoning and decision-making in long-horizon tasks.
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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