The paper introduces a distributed predictive flocking controller for a swarm of miniature vibration‑driven robots operating in a confined arena without a leader or global map. Each robot solves a nonlinear model predictive control problem in real time, optimizing cohesion, alignment, safety, and task objectives while exchanging only predicted trajectories with neighbors. Experiments on a digital twin and real robots show that a look‑ahead discrete‑time control‑barrier‑function formulation yields the fewest collisions and widest safety margins across various swarm sizes, maintaining coordination and cohesion even near the geometric packing limit.
By Prajol Shrestha, Siddhant Mohapatra, Filip Novkoski, Julian Oelhaf, Nicolas Vandewalle, Ana-Sun\v{c}ana Smith, Andreas Maier
arXiv:2607. 12861v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications.
By Yize Mi, Jianan Li, Liang Li, Shiyu Zhao
Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives.
arXiv:2510. 09204v4 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces.
By Simon Idoko, Prajyot Jadhav, Arun Kumar Singh
arXiv:2610.14248v1 Announce Type: cross
Abstract: Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating i...
By Manav Vora, Gokul Puthumanaillam, Aditya Penumarti, Jane Shin, Melkior Ornik
The paper introduces a conflict‑predictive variable horizon for distributed model predictive control in multi‑drone collision avoidance. Each drone locally estimates future conflicts using observed positions and confidence funnels, then selects the minimal horizon that covers the farthest predicted conflict, shrinking in clear air and expanding only when necessary. The authors prove that this adaptive horizon preserves recursive feasibility and asymptotic stability for linear models, and demonstrate in simulation that it reduces per‑step solver cost and total computation while maintaining separation on dense benchmarks.
By Linda M\"{u}mken, Michael Schwung, Stefan Lier, Andreas Schwung
arXiv:2610.16522v2 Announce Type: replace-cross
Abstract: Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle ma...
By Bojan Deraji\'c
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
By Cheng Guo, Mingzhe Ni, Zheng Liang, Yihu Ling, Yuan Hu, Michele Caprio, Daniele Pucci, Wei Pan
The paper compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) for cooperative motion planning of dual‑arm space manipulators tasked with reaching a target while avoiding inter‑arm collisions. A centralized state representation and multi‑objective reward function are used, and SAC consistently outperforms PPO across five random seeds, achieving an 80% success rate within 1.6 M steps versus PPO’s failure to reach this threshold within 4 M steps. Both algorithms degrade on unseen boundary targets, but SAC retains most of its performance while PPO collapses, revealing a reward‑loitering failure mode for PPO.
whyItMatters":"The study demonstrates that off‑policy SAC is more robust and effective than on‑policy PPO for coordinating dual‑arm space robots, informing algorithm choice for active debris removal and on‑orbit servicing tasks."
By Nasim Fallahiarezoodar, Zheng H. Zhu
arXiv:2607. 00444v1 Announce Type: cross Abstract: Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained.
By Jingtao Tang, Zining Mao, Lufan Yang, Hang Ma
arXiv:2509. 23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge.
By Manan Tayal, Aditya Singh, Shishir Kolathaya, Somil Bansal
arXiv:2609.16075v1 Announce Type: cross
Abstract: Flexible robotic production requires joint decisions on process progression, material routing, resource assignment, temporary cooperation, and simult...
By Fouad Bahrpeyma, David Heik, Dirk Reichelt