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Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency
(2022)2022 International Conference on Robotics and Automation (ICRA)For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation for multi-resolution volumetric mapping in changing environments. By leveraging high-level information for 3D reconstruction, our proposed ...Conference Paper -
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
(2022)2022 International Conference on Robotics and Automation (ICRA)Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth estimation and normal estimation. These different tasks provide rich information for the whole robotic perception system. All tasks have their own characteristics while sharing some latent correlations. However, some of the task predictions may suffer from the ...Conference Paper -
Collaborative Robot Mapping using Spectral Graph Analysis
(2022)2022 International Conference on Robotics and Automation (ICRA)In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global map. However, inconsistencies between onboard and server estimates can occur due to onboard odometry drift or failure. Furthermore, robots ...Conference Paper -
Neural Implicit Vision-Language Feature Fields
(2023)2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method. Our method builds on the insight that we can fuse image ...Conference Paper -
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
(2023)2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels. While impressive, these methods still require a relatively large amount of supervision and segmenting an object can take several minutes in practice. Such systems typically only optimize the representation on the scene they are fitting, without leveraging prior ...Conference Paper -
Material-agnostic Shaping of Granular Materials with Optimal Transport
(2023)2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)From construction materials, such as sand or asphalt, to kitchen ingredients, like rice, sugar, or salt; the world is full of granular materials. Despite impressive progress in robotic manipulation, manipulating and interacting with granular material remains a challenge due to difficulties in perceiving, representing, modelling, and planning for these variable materials that have complex internal dynamics. While some prior work has looked ...Conference Paper -
Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels (ASVs) in urban canals. We build upon a sampling-based method, namely Model Predictive Path Integral control (MPPI), and employ it to, in each ...Conference Paper -
On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the standard FSM towards BTs. Many works in the literature have highlighted and proven the benefits of such design compared to standard approaches, ...Conference Paper -
Credible Online Dynamics Learning for Hybrid UAVs
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Hybrid unmanned aerial vehicles (H-UAVs) are highly versatile platforms with the ability to transition between rotary- and fixed-wing flight. However, their (aero)dynamics tend to be highly nonlinear which increases the risk of introducing safety-critical modeling errors in a controller. Designing a safe, yet not too cautious controller, requires a credible model which provides accurate dynamics uncertainty quantification. We present a ...Conference Paper -
Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Interactions with articulated objects are a challenging but important task for mobile robots. To tackle this challenge, we propose a novel closed-loop control pipeline, which integrates manipulation priors from affordance estimation with sampling-based whole-body control. We introduce the concept of agent-aware affordances which fully reflect the agent's capabilities and embodiment and we show that they outperform their state-of-the-art ...Conference Paper