arXiv Machine Learning

Task-Distribution-Aware Counterweight Synthesis and Constrained Co-Design for Serial Manipulators

arXiv AI
Sep 18

Drag-Aware Aerodynamic Manipulability for Torque-Limited Redundant Multirotors: Aerodynamic Promptness based on the Symmetric Acceleration Capacity

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.

By Antonio Franchi
arXiv AI
Jun 17

Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators

arXiv:2606. 17317v1 Announce Type: cross Abstract: Real-time trajectory generation for on-orbit robotic servicing is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, visibility cone, and trajectory-level safety constraints.

By Yuji Takubo, Maximilian Adang, Mac Schwager, Simone D'Amico
arXiv Machine Learning
Jun 11

Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models

arXiv:2606. 11266v1 Announce Type: new Abstract: The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange multiplier of PPO-Lagrangian grows only after the episode budget has been exceeded.

By Samuel Tetteh, Cody Fleming