Video Friday: Albatross Falls, Spins, Self-Rights, and Sails Away
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.CoRL 2026: 9–12 November 2026, AUSTINHumanoids 2026: 6–9 December 2026, SANTA CLARA, CAEnjoy today’s videos! We’ve…
ForexNav: Foresight Exploratory Navigation in Complex and Unknown Indoor Environments
Autonomous navigation in unknown, complex indoor environments remains challenging due to limited sensing range and severe partial observability. Conventional methods rely on local maps without foresight, causing dead-ends and long detours, while local goal selection based on Euclidean distance or frontier coverage fails to balance efficiency with directionality. To address these challenges, we propose ForexNav,…
LOIC: Open-World Instance Classification in LiDAR Point Clouds
Open-world instance classification in LiDAR point clouds aims to discover and categorize instances of unknown classes unseen during training, without access to their ground-truth labels. The task is extremely challenging since only known-class labels are available for training. Existing LiDAR classification methods often depend on restrictive priors, including a predefined number of novel classes or…
An Equivariant Filter-Based State Estimator for Tightly-Coupled LiDAR–Radar–Inertial Odometry
Over the past decade, the fusion of light detection and ranging (LiDAR) and inertial navigation systems (INS) has become a reliable solution for environmental perception in intelligent mobile platforms. However, LiDAR performance degrades under adverse weather conditions. Millimeter-wave radar offers complementary benefits: it is robust to environmental disturbances and provides direct Doppler velocity measurements. Moreover,…
MAPF-World: Action World Model for Multi-Agent Path Finding
Multi-agent path finding (MAPF) studies the problem of planning conflict-free paths for multiple agents from given start locations to designated goals, with applications in robot-assisted logistics and social navigation. Recent decentralized learned solvers have shown promise for large-scale MAPF, particularly when leveraging foundation models and large datasets. However, most existing methods rely on reactive policies,…
CLEAR: A Semantic–Geometric Terrain Abstraction for Large-Scale Unstructured Environments
Long-horizon navigation in unstructured environments demands terrain abstractions that scale to tens of square kilometers while preserving semantic and geometric structure. Grids scale poorly and quadtrees misalign with terrain boundaries. Neither encodes terrain semantics essential for traversability-aware planning, yielding infeasible or inefficient paths for autonomous ground vehicles operating over 10+ km$^{2}$. CLEAR (Connected Landcover Elevation…
CAVERS: Multimodal SLAM Data From a Natural Karstic Cave With Ground Truth Motion Capture
Autonomous robots operating in natural karstic caves face perception and navigation challenges that are qualitatively distinct from those encountered in mines or tunnels: irregular geometry, reflective wet surfaces, near-zero ambient light, and complex branching passages. Yet publicly available datasets targeting this environment remain scarce and offer limited sensing modalities and environmental diversity. We present CAVERS,…
LSTP-Nav: Lightweight Spatiotemporal Policy for Map-Free Multi-Agent Navigation With LiDAR
Safe map-free multi-robot navigation requires robots to make real-time decisions from partial and noisy local observations in dynamic, unstructured environments. Existing approaches often depend on prior maps, computationally intensive perception pipelines, or carefully tuned interaction models, which limit their robustness on resource-constrained platforms. This paper proposes LSTP-Nav, a lightweight, decentralized navigation framework built on LSTP-Net…
LLM-Advisor: An LLM Advisor for Cost-Efficient Path Planning Across Multiple Terrains
This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may remain optimal within that space while missing lower-cost terrain corridors available in the native-resolution map. We propose LLM-Advisor, an external verification-guided multimodal refinement framework that uses semantic terrain context to propose route alternatives with…
CoIN: Interactive Navigation With Counterfactual Reasoning via Vision–Language Models
Interactive navigation requires robots to actively modify cluttered environments to create traversable paths, going beyond passive obstacle avoidance. However, existing methods either depend on global maps and lack the reasoning capabilities to make interaction decisions from local observations, or are restricted to interactions with simple geometric objects, limiting their applicability in partially observable, unstructured environments.…