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1,589 papers
The benefits of integrating prior bias when considering locomotion tasks with simple hinge actuators are demonstrated and a Parameter Impact metric is introduced which showcases diminishing returns for MLPs but not for CPGs.
Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.
OceanGym is introduced, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments, and reveals substantial gaps between state-of-the-art MLLM-driven agents and human experts.
This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design and analyzes how efficiency constraints reshaping model design choices in practice affects deployability, robustness, and safety.
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A tendon-driven anthropomorphic hand that is released simulation-ready, and a reinforcement learning package that lets a policy be trained entirely in simulation and run on the hand with no fine-tuning and no state estimation is presented.
PanelShield is a verifiable closed-loop safety planning framework for manual-guided industrial panel operation that balances flexibility, safety, and auditability and improves complex safety-constrained task performance over foundation-model-only planning baselines.
MaCoPlanner is presented, a task-planning framework built on knowledge compiled from equipment manuals that converts equipment manuals into a typed intermediate representation, retrieves task- and state-relevant evidence, and uses it to support plan generation.
A real-time semantic navigation framework for Unmanned Aerial Vehicles (UAVs) focused on improving time efficiency in the Object Goal Navigation (ObjectNav) task, using a Large Language Model that interprets user-provided natural language instructions and performs semantic reasoning over detected objects and spatial co...
This work presents a training-free suction grasping system for sorting deformed aseptic beverage cartons, decoupling target identification from grasp-point selection, and compares three geometric methods: k-nearest-neighbour PCA, Sobel cross-product, and RANSAC plane fitting.
The developed CARDINAL (Cardiovascular Assessment via Representation learning from Deep Imaging with Nested Anatomical Latent embeddings), a clinically grounded framework that learns compact representations from routine non-contrast cardiac CT for major adverse cardiovascular event (MACE) prediction, suggests that non-...
PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations, demonstrating that privileged latent dynamics alignment provides an effective training signal for improving anticipatory reasoning in VLA polic...
It is shown that ASR errors lead to significant safety risks for embodied AI, and automatic correction of ASR errors can reduce the risk, but this is not always effective.
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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