Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
arXiv:2603. 03741v2 Announce Type: replace-cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL).
arXiv:2511. 10276v2 Announce Type: replace-cross Abstract: Most existing robotic manipulation benchmarks focus on tabletop or household scenarios.
arXiv:2606. 01473v1 Announce Type: new Abstract: This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system, translating prefrontal EEG activity into adaptive music.
arXiv:2605. 29107v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly rank products, documents, and recommendations for user queries, which makes manipulating these rankings a growing concern for fairness and information integrity.
arXiv:2606. 00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior manifold.
arXiv:2604. 10169v2 Announce Type: replace Abstract: Trajectory prediction is a key component of autonomous driving systems because future motions directly affect collision checking, behavior planning, and control.
arXiv:2503. 15371v2 Announce Type: replace-cross Abstract: Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities.
arXiv:2606. 00702v1 Announce Type: cross Abstract: We propose to turn generalist multi-embodiment value functions into reusable models for robot design.
arXiv:2606. 00109v1 Announce Type: cross Abstract: Coronary guidewire endpoint localization is a fundamental capability for computer-assisted PCI, and its importance increases as robot-assisted PCI is progressively adopted to reduce operator radiation exposure.
arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.
arXiv:2406. 09953v4 Announce Type: replace-cross Abstract: Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies.
arXiv:2606. 01063v1 Announce Type: new Abstract: Theory of Mind (ToM) enables an agent to reason about another actor's beliefs, goals, and intentions, which is essential for human-centered embodied assistance.
arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.
arXiv:2606. 00807v1 Announce Type: new Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents.
arXiv:2605. 26310v2 Announce Type: replace Abstract: The detection of unmanned aerial vehicles (UAVs) is important for the protection of civilian and military infrastructure.
arXiv:2604. 09877v2 Announce Type: replace-cross Abstract: At the intersection of computer vision and robotic perception, 4D reconstruction of dynamic scenes connects low-level geometric sensing with high-level semantic understanding.
arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.
arXiv:2606. 00827v1 Announce Type: cross Abstract: Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models.
arXiv:2606. 02027v1 Announce Type: cross Abstract: Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments.