Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
arXiv:2607. 10110v1 Announce Type: new Abstract: Recent work on looped language models suggests that many reasoning problems benefit from greater computational depth rather than from additional independent parameters.
arXiv:2607. 11063v1 Announce Type: new Abstract: Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances.
arXiv:2607. 09792v1 Announce Type: cross Abstract: Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness in open and uncertain real-world environments.
arXiv:2607. 11689v1 Announce Type: cross Abstract: Artificial general intelligence ultimately requires agents that can reason and act in the physical world.
arXiv:2510. 09497v2 Announce Type: replace-cross Abstract: In endovascular surgery, endovascular interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels to treat various conditions such as blood clots, aneurysms, and malformations.
arXiv:2607. 10565v1 Announce Type: cross Abstract: End-to-end motion planning has emerged as a promising paradigm in autonomous driving, directly mapping raw sensor data to control commands via deep neural networks.
arXiv:2601. 15041v2 Announce Type: replace Abstract: The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios.
arXiv:2607. 09772v1 Announce Type: cross Abstract: Autonomous driving systems require reliable safety validation before real-world deployment.
arXiv:2607. 09218v2 Announce Type: replace-cross Abstract: Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break.
arXiv:2607. 11270v1 Announce Type: cross Abstract: Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities.
arXiv:2605. 14712v2 Announce Type: replace-cross Abstract: Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context.
arXiv:2607. 10172v1 Announce Type: cross Abstract: Deploying billion-parameter Vision-Language-Action (VLA) models on industrial hardware requires fine-tuning to bridge the embodiment gap.
arXiv:2607. 09825v1 Announce Type: cross Abstract: Robotic manipulation policies rely on pre-trained vision models that give either a global scene embedding or a dense patch grid.
arXiv:2607. 09795v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have been successfully adopted in various fields, including wireless communications, robotics, and autonomous vehicles, owing to their outstanding adaptability and reasoning abilities.
arXiv:2506. 14411v2 Announce Type: replace-cross Abstract: In standard reinforcement learning (RL) settings, the interaction between the agent and the environment is typically modeled as a Markov decision process (MDP), which assumes that the agent observes the system state instantaneously, selects an action without delay, and executes it immediately.
arXiv:2607. 09787v1 Announce Type: cross Abstract: LiDAR semantic segmentation is a key perception task in autonomous driving, where false predictions can affect downstream planning and safety-critical decision-making.
arXiv:2607. 10649v1 Announce Type: cross Abstract: Coverage path planning (CPP) is a fundamental problem in robot motion planning, whose aim is to produce robot trajectories that provide complete coverage of target workspaces while minimizing task-specific objectives such as path length, overlap, number of turns, and energy consumption.
arXiv:2601. 15353v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has achieved remarkable success in real-world decision-making across diverse domains, including gaming, robotics, online advertising, public health, and natural language processing.
arXiv:2606. 21587v2 Announce Type: replace-cross Abstract: Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency.
arXiv:2607. 09785v1 Announce Type: cross Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams.