The paper introduces a capacity‑aware extension of aerodynamic promptness for redundant multirotors with heterogeneous rotors. It defines the symmetric acceleration capacity (SAC) as the largest zero‑centered rotor‑acceleration interval under bounded torque and drag, and uses SAC to induce a Riemannian metric that yields a state‑attached task‑rate capability matrix and ellipsoid. The resulting drag‑aware aerodynamic manipulability (DAAM) index and its fiberwise maximization provide a task‑coordinate‑invariant criterion for selecting actuator states, demonstrated in low‑dimensional rotor studies and a two‑rotor allocation case that improves force‑tracking performance compared to the pseudoinverse.
arXiv:2609.15082v1 Announce Type: cross Abstract: Passive counterweights are simple gravity compensators, but a counterweight selected from a single pose is not generally optimal for the configuratio...
Passive counterweights are simple gravity compensators, but a counterweight selected from a single pose is not generally optimal for the configurations and tasks a manipulator actually executes. This...
arXiv:2606. 12728v2 Announce Type: replace-cross Abstract: Most learned dexterous grasp generators relegate contact forces to a downstream verification step, so a kinematically-plausible pose can still violate the conditions for a stable physical grasp.
arXiv:2606. 10857v1 Announce Type: cross Abstract: We present a generalist position control policy capable of controlling arbitrary multirotor configurations of a certain rotor count (e.
arXiv:2503. 08394v5 Announce Type: replace-cross Abstract: Multi-task optimization is typically characterized by a fixed and finite set of tasks.
UniConFlow is a unified constrained flow‑matching framework that generates robot trajectories while systematically handling both equality and inequality constraints such as collision avoidance, actuation limits, and dynamic consistency. It introduces a prescribed‑time zeroing function to shape a time‑varying guidance field during inference, and employs a violation‑segment extraction protocol and trajectory compression to address computational challenges in long‑horizon, high‑dimensional planning. Experiments on a double inverted pendulum, a real‑to‑sim car racing task, and a sim‑to‑real manipulation task show that UniConFlow outperforms state‑of‑the‑art generative planners and conventional optimization baselines on certified motion planning metrics like safety, kinodynamic consistency, and action feasibility.
arXiv:2602.02269v2 Announce Type: replace-cross Abstract: We present $multipanda\_ros2$, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 contr...
arXiv:2610. 13773v1 Announce Type: cross Abstract: Multiple-gradient descent is typically formulated in Euclidean geometry, whereas matrix-aware optimizers use directions induced by other norms.
arXiv:2607. 07204v2 Announce Type: replace-cross Abstract: Structured preconditioners restrict optimization to a small family of positive metrics, but endpoint condition-number reachability does not measure the geometric effort required to reach a useful metric.
arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.
arXiv:2606. 16564v1 Announce Type: cross Abstract: Robotic systems routinely encounter conflicting objectives, modeling errors, and degenerate contact conditions that render quadratic programs (QPs) infeasible.
The paper introduces VATO, a Vortex-Force-Aware Transformer Operator designed to predict unsteady separated flows around aerofoils more accurately. VATO couples a Vortex Force Map (VFM) method with a geometry-aware neural operator, offering two variants: VATO‑S, which adds training-only supervision of local VFM force contributions, and VATO‑A, which prioritizes force-relevant source locations for residual cross attention. Evaluated on CFD data for double‑edged‑plate aerofoils, VATO‑S and VATO‑A reduce velocity, pressure, and vorticity errors by up to 15.8%, 7.5%, and 31.2% respectively, and improve aerodynamic force predictions even beyond the training range.