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新论文 · v1arXiv:2611.14685

Terrain-Aware Thompson Sampling for Multi-Drone Search and Rescue

Riana Gagnon Souleiman, Giovanni Beltrame

原文摘要

In this work, we propose a decentralized terrain-aware multi-drone Thompson Sampling algorithm tailored for search and rescue (SAR) applications. A probability distribution of locating a lost person is informed by terrain data, using established SAR models (displacement ring, elevation, watershed, dispersion angle, and track offset) derived from historical SAR case data on lost person behavior. Exploration is conducted by a team of drones using Thompson Sampling, where candidate actions are selected by drawing samples from this probability distribution, represented by a per-cell Gaussian posterior whose mean and variance are updated through Bayesian inference. This approach balances exploration of uncertain regions with exploitation of high-probability areas to locate a lost person. Observation uncertainty is incorporated through fractional forest coverage, which measures the percentage of an observation occluded by tree coverage and accounts for reduced detection performance in densely vegetated areas. Simulations using real SAR case data (YOSAR) evaluate the proposed method against a coverage planner and a probabilistic active search method in search regions of 1.5 and 3.8 square kilometers, with 5, 10, 15, and 20 agents flying at 50 km/hr. We demonstrate that our method achieves a success rate of approximately 87% within 60 to 247 seconds in the 1.5 square kilometer region, and success rates of approximately 75% to 91% within 161 to 466 seconds in the 3.8 square kilometer region, across different agent configurations.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05244

表达性集群机器人中的现实差距与感知差距

Reality and Perceptual Gaps in Expressive Swarm Robotics

Ali Imran, Abdalwhab Bakheet Mohammed Abdalwhab, Rafael Gomes Braga, Hélène Duval, Audrey Rochette, Vivek Shankar Varadharajan, Yann Bouteiller, Jean Mazerolle 等 10 位作者

AI 速读 基于摘要

同一套去中心化协调代码在三类机器人集群上现场演出时，作者报告硬件实现会显著改变可见运动，且观众的意义归因与情绪反应并不一致。

cs.RO

arXiv ↗PDF ↗
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Moving robot swarms from simulation into the physical world can be difficult because small discrepancies in hardware, timing and communication can cascade across interacting robots and alter group behaviour. This challenge is known as the reality gap. When swarms operate in human environments, a second challenge emerges: a perceptual gap between what the swarm physically does and what human onlookers actually perceive. This study examines the same decentralised coordination code deployed across three robotic swarms-tabletop, aerial and human-scale mobile manipulators-during live artistic performances. We find that while the underlying algorithms executed reliably on all three platforms, physical embodiment drastically altered visible motion-distorting execution timescales by more than a factor of three and reshaping trajectory geometry. More importantly, audience surveys revealed that human interpretation does not mirror technical success: how observers attribute meaning (perceived purpose) and how they react emotionally (comfort versus threat) depend heavily on physical form. Meaning attribution and emotional responses did not vary together. Our results demonstrate that successfully transferring swarm software to physical hardware is insufficient for real-world deployment. Addressing the reality gap does not guarantee that the resulting collective motion will be readily interpreted by human observers. Swarms destined for human environments must be engineered not only for decentralised stability but also for the expressive physical dynamics through which humans interpret collective motion.

版本更新 · v3arXiv:2606.20627

Latent Goal Prediction from Language for Model-Based Planning

Samuel Barbeau, Simon Roy, Giovanni Beltrame, Christian Desrosiers, Nicolas Thome

原文摘要

Joint-Embedding Predictive Architectures (JEPAs) enable agents to plan in latent space by imagining the outcomes of candidate actions, yet task specification remains a bottleneck. Visual targets provide precise local gradients but poor distant guidance, while language is flexible yet limited by noisy cross-modal alignment or dependence on distinct large generative models. We introduce LAGO (Latent Goal Prediction from Language), a hierarchical world model in which a single predictor both forecasts action-conditioned dynamics and grounds language instructions as sequences of intermediate latent subgoals, training both modes with a single regression objective over a shared latent space. At each planning step, LAGO predicts a sequence of latent subgoals from a language instruction and optimizes an action sequence using a soft-minimum alignment cost that rewards subgoal proximity without enforcing a rigid path. Subgoals are repredicted as the agent's state evolves, turning a single long-horizon instruction into a sequence of locally tractable objectives. Across environments spanning navigation and manipulation, LAGO outperforms flat and hierarchical image-goal planners, with the largest gains on long-horizon navigation with curved, hazard-constrained paths, where it more than doubles the success rate of a goal-conditioned behavior cloning policy trained on the same demonstrations. Grounding language in the world model's own latent space also outperforms vision-language reward models, including one that plans with ground-truth dynamics.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28571

Finding Change in Satellite Archives from Text: How to Combine Before-and-After Images Efficiently

Simon Roy, Mark Bong, Giovanni Beltrame

原文摘要

Operational Earth observation increasingly calls for answering queries such as ``find the image pairs where a new building appeared.'' This means searching an archive of before-and-after (bi-temporal) satellite image pairs and ranking each pair by how well it matches a natural-language description of the change. The component that performs this match, the fusion module that combines the ``before'' and ``after'' views, must be run at query time across many candidate pairs, so its speed largely sets the cost of every search. We present a controlled comparison of how to build that module. Using one fixed image encoder (a frozen CLIP model) and one training recipe for all variants, we evaluate eight designs drawn from three families: attention, state-space models (Mamba), and learned compression (our Temporal Bottleneck Fusion, TBF). Each design is tested on two benchmarks (LEVIR-CC and Dubai-CC) with ten random seeds, so the reported differences are statistically grounded. We outline three findings: first, a training-free two-stage search (a cheap difference model that shortlists candidates, followed by attention fusion that re-ranks them) matches or exceeds full-fusion recall on LEVIR-CC while cutting query cost $10$-$15\times$, with comparable R@1/R@5 on Dubai-CC; second, the linear-time scan of Mamba, attractive on paper, gives no speed benefit at the patch counts typical of vision transformers ($L{=}196$): the scan is limited by memory bandwidth, whereas attention maps cleanly onto parallel hardware; and third, compressing the fused representation (TBF) reduces parameters by $2.3\times$ and latency by $1.6\times$ for a change-only BLEU-1 cost of $0.007$, although more aggressive compression quietly discards change-relevant detail that aggregate metrics fail to reveal.

cs.CV · cs.IR

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arXiv ↗PDF ↗