Best of ICRA - July 21, 2026

The Best of ICRA is a three-day virtual meetup series featuring researchers presenting their accepted papers from the 2026 International Conference on Robotics and Automation (ICRA).

Each session features a curated lineup of speakers sharing cutting-edge research across robotics, computer vision, and AI — straight from papers accepted at one of the field's top conferences.

Whether you're a researcher, engineer, or practitioner, you'll leave with a sharper view of where the field is heading.

Autonomous robots in safety-critical settings require geometric perception that is not merely accurate on average, but provably correct under adversarial conditions. Yet most pipelines rely on local optimization methods that fail silently when poorly initialized.

This talk presents GlobustVP, a certifiably optimal vanishing point estimator that reformulates joint VP localization and line association as a quadratically constrained quadratic program (QCQP) and relaxes it to a tight semidefinite program (SDP), achieving the first globally optimal and outlier-robust solution to this problem. Recognized as a Best Paper Award Candidate at CVPR 2025 (top 0.1%, 14 of 13,008 submissions), GlobustVP demonstrates that certifiable global optimization is both practically feasible and highly competitive.

More broadly, this work is part of a research program toward trustworthy geometric perception: systems that know when they are wrong, and can communicate that to the robots and humans that depend on them.

Outdoor robot navigation in the unstructured world requires robots to reason about more than obstacles: they must understand where they can move, what terrain is suitable, and how scene context affects navigation decisions. In sidewalks, campuses, trails, and off-road environments, these decisions depend on geometric structure, terrain conditions, semantic cues, and robot-environment interaction.

In this talk, I will present our recent work on scene understanding for outdoor navigation, including a large-scale multimodal dataset for studying outdoor traversability, approaches for trajectory generation and selection, vision-language reasoning for contextual navigation, and Gaussian-based 3D scene modeling. I will also discuss how physical reasoning can extend scene understanding from visual and geometric perception toward terrain properties and interaction cues.

Together, these works explore how robots can better interpret unstructured outdoor environments and use that understanding for navigation decision-making.

In this talk, I will question whether 3D reconstruction is still a necessary part of mapping in the age of feedforward models and present some alternatives. Then, I discuss scene graphs as an alternative map representation and their applications for mobile manipulation.

Robust 6D pose estimation of textured objects under diverse illumination conditions remains a significant challenge, often requiring a trade-off between accurate initial pose estimation and efficient real-time tracking. We present a unified framework explicitly designed for efficient execution on edge devices, which fuses a robust initial estimation module with a fast motion-based tracker.

The key to our approach is a shared, lighting-invariant color-pair feature representation that forms a consistent foundation for both stages. For initial estimation, this representation facilitates robust registration between the live RGB-D view and the object's 3D mesh.

For tracking, the same representation validates temporal correspondences, enabling a lightweight model to reliably regress the object's pose. Experiments on benchmark datasets demonstrate that our integrated approach is both effective and robust, providing competitive pose estimation accuracy while maintaining high-fidelity tracking even through abrupt pose changes.

This is joint work with Xingjian Yang.