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1,183 papers
It is found that the MPA significantly influenced the PMPA and perceived virtue scores, and the best-fitting model to describe the relationship between PMPA and PVS was cubic, which is symmetrical in nature.
The results clearly demonstrate that FlowCorrect learns from very few demonstrations and enables fast, sample-efficient, incremental, human-in-the-loop corrections of generative visuomotor policies at deployment time in real-world robotics.
This system discovers causally linked action pairs directly from the training data, where one action produces exactly the condition that the next one requires, and turns each pair into a new operator, and not only accelerates planning but, in certain domains, determines solvability in practice.
Meta-Ctrl is proposed, a constrained-decoding framework that guarantees the encoded constraints while preserving the base LM's plan quality, and is demonstrated on a real tabletop robot, where every generated plan satisfies its preconditions and goals by construction.
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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.
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...
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
Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
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