arXiv:2608. 16651v1 Announce Type: cross Abstract: Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations.
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).
The paper presents a reward‑free continual learning framework for space robots that uses latent‑state world models to adapt to severe hardware degradation. By pre‑training a model‑based agent in diverse simulations, the world model learns to predict reward structure in latent space. During deployment, the observation encoder and reward predictor are frozen while only the transition dynamics are updated via unsupervised rollouts, allowing the policy to adapt using imagined trajectories without new rewards.
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.
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...
arXiv:2608. 09876v1 Announce Type: cross Abstract: Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics.
World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primarily focus on reconstructing future observations or...
The paper introduces a Latent World Model (LWM) for robot navigation that predicts action‑conditioned latent feature compatibility instead of reconstructing future observations. By exploiting the correlation between spatial proximity and latent feature similarity, the model evaluates action consequences directly in latent space and supports counterfactual training using sampled action sequences. The learned world model can supervise policy learning from unlabeled video and further improve policies via reinforcement learning entirely within the model, eliminating the need for action annotations and additional environment interaction.
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:2606. 09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM).
arXiv:2605. 08732v2 Announce Type: replace-cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging.
Latent Energy Action Planning (LEAP) is a new method that treats the entire action horizon as a differentiable variable and optimizes it using a frozen LeWorldModel (LeWM). LEAP couples terminal latent goal matching with a terminal‑window state energy, ensuring that the predicted terminal latent and decoder‑predicted terminal descriptor align with the goal. Using a frozen goal‑conditioned proposal, a quasi‑Newton solver, and post‑optimization projection, LEAP improves mean success from 77.5% to 94.8% across four control domains while keeping the LeWM representation frozen.
arXiv:2606. 06014v1 Announce Type: new Abstract: Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning.