RSS 2025 论文列表

The biomimetic research of vertebrates is challenging in both mechanism design and control methods. Motivated by natural acrobatics exhibited by cats and humans, this paper presents a generic multi-joint continuous spinal system and a learning-based algorithm for agile and accurate control. The spinal system combines flexibility with a high load-bearing capacity, rendering it suitable for various types of bionic robots. It features a chain-like structure formed by multiple pairs of spherical gear joints, which endow it with the ability to bend in all directions. Then, to realize dynamic and precious control, a universal control framework integrating online and offline learning is proposed. In this framework, Graph Neural Networks are employed to learn the dynamic model parameters of the spine offline, while the parameterized Model Predictive Control (GNN-MPC) can update the dynamic constraints online and select the optimal control strategy. In the aerial flipping task of the spinal column, a dynamic constraint analysis of the angular momentum of the spinal structure is conducted to derive the most efficient flipping strategy. It allows the spinal structure to execute flips in the air without relying on external forces or mechanical structures. Quantitative analyses of high-load applications on the spine reveal that the spinal column can maintain strength, precision and flexibility simultaneously. A series of aerial flipping experiments prove the designed spine’s scalability, flexibility and high load capacity. With GNN-MPC, the spine system can realistically mimic biological spine behavior, validating the algorithm’s effectiveness and robustness.

We consider the problem of designing a smooth trajectory that traverses a sequence of convex sets in minimum time, while satisfying given velocity and acceleration constraints. This problem is naturally formulated as a nonconvex program. To solve it, we propose a biconvex method that quickly produces an initial trajectory and iteratively refines it by solving two convex subproblems in alternation. This method converges quickly to low-cost trajectories, returns a feasible solution even if stopped early, and does not require the selection of any line-search or trust-region parameter. Exhaustive experiments show that our method can find high-quality trajectories in a fraction of the time of state-of-the-art solvers for nonconvex optimization. Additionally, tested on the problem of transferring packages between bins using two robot arms, our method achieves a fifty percent increase in throughput compared to waypoint-based motion planners that are common in industry.