rss26 papers link; shibo's science robotics paper

Author: seungchan-kim

_bibliography/references.bib

@@ -3,20 +3,24 @@ @inproceedings{zhao2026supermap
 	author 		 = {Zhao, Shibo and Chen, Guofei and Zhu, Honghao and Li, Zhiheng and Yao, Changwei and Zantout, Nader and Kim, Seungchan and Wang, Wenshan and Zhang, Ji and Scherer, Sebastian},
 	year 	     = {2026},
 	booktitle    = {Robotics: Science and Systems (RSS)},
+	link 		 = {https://www.roboticsproceedings.org/rss22/p052.pdf},
+	video 		 = {https://youtu.be/TQjTTqEewNQ?si=h8Vrl70hzbGECCq9},
 	abstract	 = {Robotic navigation in human environments requires a spatio-temporal semantic representation that can reconcile open-vocabulary perception with long-term environmental changes. While foundation models provide strong zero-shot recognition, their predictions are intermittent and view-dependent, and naively integrating them into mapping pipelines leads to identity drift and stale semantics over time. We present SuperMap, a 4D spatio-temporal mapping framework for language-guided navigation that integrates high-frequency geometric SLAM with asynchronous open-vocabulary perception. Our core contribution is a consistency-driven mapping engine that combines 3D-aware instance association/re-activation with a principled existence-and-label confidence update to maintain stable object identities and prune outdated map content under occlusions and scene changes. SuperMap produces a queryable 4D scene-graph representation that interfaces naturally with Vision-Language Models by supporting compositional queries over object semantics, relations, and history. We demonstrate SuperMap on benchmarks and real robots, including dynamic scenes with appearance/disappearance and relocation, and provide ablations and runtime analysis. We release the full system as open-source to provide the community with a deployable baseline for open-vocabulary spatio-temporal mapping.}
 }
 @inproceedings{triest2026travsuite,
 	title	  	 = {TravSUITE: Traversability via Self-Supervised, Uncertainty-Aware IRL and Terrain Estimation},
 	author 		 = {Triest, Sam and Shaban, Amirreza and Fan, David and Wang, Wenshan and Scherer, Sebastian},
 	year 	     = {2026},
 	booktitle    = {Robotics: Science and Systems (RSS)},
+	link 		 = {https://www.roboticsproceedings.org/rss22/p070.pdf},
 	abstract 	 = {Traversability analysis in off-road settings remains a fundamental challenge for mobile robots. Key difficulties include constructing an accurate, expressive local map from multi-modal sensor data and using the map to design traversability rules that yield desirable navigation behavior. Importantly, this system must be resilient to the limited sensing regime brought about by complex environments and high speeds. In this paper, we present TravSUITE, a traversability system suitable for high- speed navigation in off-road environments. TravSUITE consists of two major components: 1) a VFM-based voxel mapper that builds a rich geometric-semantic local map from streams of on-board sensor data, and 2) a unified neural network that jointly pre- dicts traversability-relevant quantities in bird’s eye view (BEV), including geometry, semantics, speed and cost. Our training strategy is entirely annotation-free and self-supervised, leveraging tasks such as map inpainting and inverse reinforcement learning (IRL) to learn both map representations and traversability. We also perform a thorough ablation study and comparison to state- of-the-art approaches, and the results indicate that cost learning and auxiliary inpainting each contribute significantly to planning quality, and their combination is critical for achieving state-of- the-art performance in path planning. We also design a simple risk adaptation mechanism to leverage our method's uncertainty estimates at deploy-time, and demonstrate that a combination of inpainting and risk estimation can result in 80% fewer navigation errors and 5% faster autonomous traversal speeds in real-world hardware experiments.}
 }
 @inproceedings{huang2026kinder,
 	title	  	 = {KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning},
 	author 		 = {Huang, Yixuan and Li, Bowen and Saxena, Vaibhav and Liang, Yichao and Mishra, Utkarsh Aashu and Ji, Liang and Zha, Lihan and Wu, Jimmy and Kumar, Nishanth and Scherer, Sebastian and Xu, Danfei and Silver, Tom},
 	year 	     = {2026},
 	booktitle    = {Robotics: Science and Systems (RSS)},
+	link 		 = {https://arxiv.org/abs/2604.25788},
 	abstract     = {Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand. We introduce KinDER, a benchmark for Kinematic and Dynamic Embodied Reasoning that targets physical reasoning challenges arising in robot learning and planning. KinDER comprises 25 procedurally generated environments, a Gymnasium-compatible Python library with parameterized skills and demonstrations, and a standardized evaluation suite with 8 implemented baselines spanning task and motion planning, imitation learning, reinforcement learning, and foundation-model-based approaches. The environments are designed to isolate five core physical reasoning challenges: basic spatial relations, nonprehensile multi-object manipulation, tool use, combinatorial geometric constraints, and dynamic constraints, disentangled from perception, language understanding, and application-specific complexity. Empirical evaluation shows that existing methods struggle to solve many of the environments, indicating substantial gaps in current approaches to physical reasoning. We additionally include real-to-sim-to-real experiments on a mobile manipulator to assess the correspondence between simulation and real-world physical interaction. KinDER is fully open-sourced and intended to enable systematic comparison across diverse paradigms for advancing physical reasoning in robotics.}
 }
 @article{moon2026ia-tigris,
@@ -93,6 +97,15 @@ @inproceedings{keetha2026mapanything
 	organization = {IEEE},
 	abstract     = {We introduce MapAnything, a unified transformer-based feed-forward model that ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions, and then directly regresses the metric 3D scene geometry and cameras. MapAnything leverages a factored representation of multi-view scene geometry, i.e., a collection of depth maps, local ray maps, camera poses, and a metric scale factor that effectively upgrades local reconstructions into a globally consistent metric frame. Standardizing the supervision and training across diverse datasets, along with flexible input augmentation, enables MapAnything to address a broad range of 3D vision tasks in a single feed-forward pass, including uncalibrated structure-from-motion, calibrated multi-view stereo, monocular depth estimation, camera localization, depth completion, and more. We provide extensive experimental analyses and model ablations demonstrating that MapAnything outperforms or matches specialist feed-forward models while offering more efficient joint training behavior, thus paving the way toward a universal 3D reconstruction backbone.}
 }
+@article{zhao2025resilient,
+	title        = {Resilient Odometry via Hierarchical Adaptation},
+	author       = {Zhao, Shibo and Zhou, Sifan and Zhang, Yuchen and Zhang, Ji and Wang, Chen and Wang, Wenshan and Scherer, Sebastian},
+	year         = {2025},
+	journal 	 = {Science Robotics},
+	link 	     = {https://www.science.org/doi/full/10.1126/scirobotics.adv1818},
+	publisher 	 = {American Association for the Advancement of Science},
+	abstract 	 = {Resilient and robust odometry is crucial for autonomous systems operating in complex and dynamic environments. Existing odometry systems often struggle with severe sensory degradations and extreme conditions such as smoke, sandstorms, snow, or low-light conditions, threatening both the safety and functionality of robots. To address these challenges, we present Super Odometry, a sensor fusion framework that dynamically adapts to varying levels of environmental degradation. Super Odometry uses a hierarchical structure to integrate four core modules from lower-level to higher-level adaptability, including adaptive feature selection, adaptive state direction selection, adaptive engine selection, and a learning-based inertial odometry. The inertial odometry, trained on more than 100 hours of heterogeneous robotic platforms, captures comprehensive motion dynamics. Super Odometry elevates the inertial measurement unit to equal importance with camera and light detection and ranging (LiDAR) systems in the sensor fusion framework, providing a reliable fallback when exteroceptive sensors fail. Super Odometry has been validated across 200 kilometers and 800 operational hours on a fleet of aerial, wheeled, and legged robots and under diverse sensor configurations, environmental degradation, and aggressive motion profiles. It marks an important step toward safe and long-term robotic autonomy in all-degraded environments.}
+}
 @inproceedings{zhang2025ufm,
 	title        = {{UFM}: A Simple Path towards Unified Dense Correspondence with Flow},
 	author       = {Zhang, Yuchen and Keetha, Nikhil and Lyu, Chenwei and Jhamb, Bhuvan and Chen, Yutian and Qiu, Yuheng and Karhade, Jay and Jha, Shreyas and Hu, Yaoyu and Ramanan, Deva and others},