arXiv Machine Learning

Neural operator learning for collision-aware trajectory planning of spacecraft swarms

arXiv:2608. 00320v1 Announce Type: new Abstract: Autonomous spacecraft swarms must plan fuel-efficient, collision-free maneuvers in increasingly congested orbits, yet classical trajectory optimization scales poorly as pairwise safety constraints multiply with swarm size, and learning-based planners rarely transfer across swarm sizes or debris densities.

arXiv Machine Learning

Sep 16

Neural Operator Learning for Collision-Aware Trajectory Planning of Spacecraft Swarms

The paper presents a permutation‑equivariant neural operator that learns to generate collision‑free, fuel‑efficient trajectories for spacecraft swarms by mapping distributions of initial and target states, as well as obstacle states, to trajectory outputs. The operator is self‑supervised and, when paired with a batched Gauss‑Newton step, enforces exact orbital dynamics and further reduces fuel consumption. Trained on ten spacecraft, the model generalizes zero‑shot to swarms of 1,000 spacecraft and 11,000 obstacles, achieving accuracy comparable to a per‑agent optimal control solver while maintaining collision avoidance.

By Sidhdharth D. Sikka, Suyi Gao, Zehui Lu, Rongjie Lai, Shaoshuai Mou

agents safety

arXiv Machine Learning

Aug 11

Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance

arXiv:2608. 09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO).

By Logan Luna (Georgia Institute of Technology), Juan Ortiz Couder (Embry-Riddle Aeronautical University), Raul Alejandro Vargas-Acosta (Embry-Riddle Aeronautical University)

agents reinforcement-learning

arXiv AI

Aug 18

Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents

arXiv:2608. 16651v1 Announce Type: cross Abstract: Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations.

By Zhijian Li, Chao Ren, Peijin Wang, Xian Sun

agents diffusion

arXiv Machine Learning

Jul 1

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization

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

llms diffusion robotics benchmarks safety

arXiv Machine Learning

Aug 28

Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation

The paper introduces Planning Diffusion Policy Optimization (PDPO), an offline‑to‑online reinforcement‑learning framework that employs a diffusion policy to produce short‑horizon action chunks for robot crowd navigation. PDPO is pretrained on collision‑avoidance demonstrations and fine‑tuned online with PPO, generating five‑step action sequences applied in a receding‑horizon manner. The authors also identify a benchmark artifact where agents can leave the valid domain without explicit boundary constraints, and they mitigate this by treating boundary violations as collisions, leading to improved success rates over strong baselines.

By Wendong Li, Jochen Garcke

agents diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 24

Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections

arXiv:2607. 21488v1 Announce Type: cross Abstract: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.

By Gil Lifshits, Igal Bilik, Gilad Katz

rag agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 17

Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations

arXiv:2606. 17414v1 Announce Type: new Abstract: Autonomous spacecraft rendezvous and proximity operations (RPO) require controllers that guarantee safety under thrust constraints while minimizing fuel expenditure.

By Alejandro Posadas-Nava, Richard Linares, Minduli Wijayatunga

agents reinforcement-learning safety

arXiv AI

Aug 24

Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight

arXiv:2608.20948v1 Announce Type: cross Abstract: Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this...

By Zhitao Liu, Guangtong Xu, Zihan Wang, Jialiang Hou, Chao Xu, Fei Gao

agents robotics benchmarks

arXiv Machine Learning

Jun 3

Neural Navigation Functions for Zero-Shot Generalizable Motion Planning

arXiv:2606. 03756v1 Announce Type: cross Abstract: We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment geometries.

By Benjamin D. Shaffer, Pei-An Hsieh, Brooks Kinch, Nathaniel Trask, M. Ani Hsieh

arXiv AI

2d ago

OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous

arXiv:2610.01093v1 Announce Type: cross Abstract: Spacecraft rendezvous and proximity operations (RPO) are currently planned through an expertise-intensive process in which engineers translate high-l...

By Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico

llms agents

arXiv AI

Jul 24

Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

arXiv:2607. 20743v1 Announce Type: cross Abstract: Trajectory planning is a fundamental problem in robotics, requiring the generation of collision-free and efficient trajectories in a potentially complex environment.

By Miroslav Krupa, Miroslav Cibula, Krist\'ina Malinovsk\'a

robotics

arXiv Machine Learning

Jun 30

Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability

arXiv:2605. 26790v3 Announce Type: replace Abstract: Low-thrust trajectory design relies heavily on repeated evaluations of fuel consumption and transfer feasibility, which require expensive optimal control solutions.

By Zhong Zhang, Giacomo Acciarini, Dario Izzo, Hexi Baoyin, Francesco Topputo

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