机器人
66 篇 · 资讯流SomaAlign: Reliability-Aware Multimodal Representation Learning for Soft Robotic Grasping
Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic ambiguity, an
原文 ↗Gym-Anything for Robotics: Scaling physical simulations with digital agents
Realistic, fast robotics simulations could enable large-scale training of embodied AI models that transfer to the real w
原文 ↗Assuring Learning-Enabled Cyber-Physical Systems with Foundation Models: A Neuro-Symbolic Agenda for Testing, Verification, and Runtime Assurance
Learning-enabled cyber-physical systems (CPS), including autonomous vehicles and UAVs, increasingly incorporate foundati
原文 ↗TAPESTRY: A Single Backbone for Video Generation and Robot Control
Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control
原文 ↗Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
Multi-objective reinforcement learning is a fundamental problem for modern AI, with applications spanning post-training,
原文 ↗FISRF: Flexible Instructional Scaffolding Reflection Framework for Robotics Task Planning with Large Language Model
Large Language Models (LLMs) are widely adopted for reflective error correction in task planning; however, existing appr
原文 ↗Dream to Assist: Intent-Conditioned World Models for Shared Vehicle Control
Assistive agents in shared-control settings face a dual challenge: acting proactively to align with the human's intentio
原文 ↗Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
Robotic foundation models are typically evaluated through costly closed-loop rollouts, which motivates cheap offline pro
原文 ↗SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets
Task and Motion Planning (TAMP) stands as a fundamental challenge in robotics, essential for enabling autonomous agents
原文 ↗Learning Robot Behavior Priors from Non-Robotic Synthetic Data
Real robot data, rich across tasks and morphologies, is often treated as the bottleneck for training robot learning mode
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