arXiv:2606. 31941v1 Announce Type: cross Abstract: Unstructured navigational features, such as irregular planting or discontinuities, remain the primary failure mode for under-canopy agricultural robots.
arXiv:2607. 16314v1 Announce Type: cross Abstract: World models, especially based on JEPA architectures, have been shown to learn robust dynamics of various environments.
arXiv:2610.16558v1 Announce Type: new Abstract: World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies beyo...
The paper introduces FOLIAGE, a geometry‑centered latent world model designed for systems that grow and change shape over time. FOLIAGE allocates higher‑resolution state to regions expected to grow while keeping mature areas compact, fusing RGB, point‑cloud, and mesh data into a deployable geometric state and conditioning dynamics on material controls. The authors evaluate the model on new benchmarks (SURF‑GARDEN and SURF‑BENCH) and demonstrate significant reductions in inverse‑material error, mesh forecasting error, and improved cross‑modal retrieval, with robust performance under sensor loss and correspondence corruption.
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
arXiv:2606.20627v2 Announce Type: replace Abstract: Joint-Embedding Predictive Architectures (JEPAs) enable agents to plan in latent space by imagining the outcomes of candidate actions, yet task spe...
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.
arXiv:2606. 18634v1 Announce Type: cross Abstract: To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots.
arXiv:2608.24855v1 Announce Type: new Abstract: Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online traje...
arXiv:2610.29845v1 Announce Type: cross Abstract: Autonomous mobile robots operating in dense crowds and cluttered sites require $360^\circ$ perception to react to surrounding hazards. While panorami...
Reading the Room (RR) is a global‑local method that registers scale‑ambiguous, sparse semantic VSLAM maps to partially built floor plans. It introduces a single robust, differentiable, class‑balanced semantic‑geometric energy function that drives all pipeline stages, and offers two global initialization schemes—one using a single user‑provided correspondence and another using a cross‑modal matching network. Evaluated on 33 sequences from three construction sites, RR outperforms classical and learning‑based baselines, improving mean absolute trajectory error by 4.5× overall and 7.6× when an anchor is supplied.
ObstaDiff is a diffusion-policy framework that introduces a lightweight obstacle-aware visual encoder to generate structured representations of targets, obstacles, and background. By aligning these representations, the policy produces end-effector trajectories that focus on a target-centered bottleneck pose while accounting for surrounding obstacles. In real-robot greenhouse trials, ObstaDiff achieved a 75.41% task success rate and an 8.20% obstacle collision rate, outperforming existing imitation-learning baselines in cluttered agricultural settings.
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.