ICRA 2021 Papers

The Cosserat rod equations are used to model continuum and soft robots. Solving these equations are computationally expensive, particularly due to mixed boundary values and kinematic constraints. In this paper, we present a novel nonlinear observer that can rapidly estimate the solution of the Cosserat rod equations. We present details of the observer design and analyse its convergence and stability. Furthermore, we compare the accuracy and performance of the observer with common solvers used in the literature. Our results show that the proposed observer can significantly improve the computational efficiency of continuum robots’ models and estimates the solution of the Cosserat rod equations 7 times faster than common solvers.

Deep Convolutional Neural Networks (CNNs) have been successfully deployed on robots for 6-DoF object pose estimation through visual perception. However, obtaining labeled data on a scale required for the supervised training of CNNs is a difficult task - exacerbated if the object is novel and a 3D model is unavailable. To this end, this work presents an approach for rapidly generating real-world, pose-annotated RGB-D data for unknown objects. Our method not only circumvents the need for a prior 3D object model (textured or otherwise) but also bypasses complicated setups of fiducial markers, turntables, and sensors. With the help of a human user, we first source minimalistic labelings of an ordered set of arbitrarily chosen keypoints over a set of RGB-D videos. Then, by solving an optimization problem, we combine these labels under a world frame to recover a sparse, keypoint-based representation of the object. The sparse representation leads to the development of a dense model and the pose labels for each image frame in the set of scenes. We show that the sparse model can also be efficiently used for scaling to a large number of new scenes. We demonstrate the practicality of the generated labeled dataset by training a CNN based 6-DoF object pose estimator.

In this paper, we investigate how heterogeneous multi-robot systems with different sensing capabilities can observe a domain with an a priori unknown density function. Common coverage control techniques are targeted towards homogeneous teams of robots and do not consider what happens when the sensing capabilities of the robots are vastly different. This work proposes an extension to Lloyd’s algorithm that fuses coverage information from heterogeneous robots with differing sensing capabilities to effectively observe a domain. Namely, we study a bimodal team of robots consisting of aerial and ground agents. In our problem formulation we use aerial robots with coarse domain sensors to approximate the number of ground robots needed within their sensing region to effectively cover it. This information is relayed to ground robots, who perform an extension to Lloyd’s algorithm that balances a locally focused coverage controller with a globally focused distribution controller. The stability of the Lloyd’s algorithm extension is proven and its performance is evaluated through simulation and experiments using the Robotarium, a remotely-accessible, multi-robot testbed.

Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular mesh. We use the Poisson surface reconstruction to generate the mesh-based map representation. Based on the range images generated from the current LiDAR scan and the synthetic rendered views from the mesh-based map, we propose a new observation model and integrate it into a Monte Carlo localization framework, which achieves better localization performance and generalizes well to different environments. We test the proposed localization approach on multiple datasets collected in different environments with different LiDAR scanners. The experimental results show that our method can reliably and accurately localize a mobile system in different environments and operate online at the LiDAR sensor frame rate to track the vehicle pose.

Relocalization is a fundamental task in the field of robotics and computer vision. There is considerable work in the field of deep camera relocalization, which directly estimates poses from raw images. However, learning-based methods have not yet been applied to the radar sensory data. In this work, we investigate how to exploit deep learning to predict global poses from Emerging Frequency-Modulated Continuous Wave (FMCW) radar scans. Specifically, we propose a novel end-to-end neural network with self-attention, termed RadarLoc, which is able to estimate 6-DoF global poses directly. We also propose to improve the localization performance by utilizing geometric constraints between radar scans. We validate our approach on the recently released challenging outdoor dataset Oxford Radar RobotCar. Comprehensive experiments demonstrate that the proposed method outperforms radar-based localization and deep camera relocalization methods by a significant margin.

Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user’s utility landscape. Learning these landscapes is challenging, as walking trajectories are defined by numerous gait parameters, data collection from human trials is expensive, and user safety and comfort must be ensured. This work proposes the Region of Interest Active Learning (ROIAL) framework, which actively learns each user’s underlying utility function over a region of interest that ensures safety and comfort. ROIAL learns from ordinal and preference feedback, which are more reliable feedback mechanisms than absolute numerical scores. The algorithm’s performance is evaluated both in simulation and experimentally for three non-disabled subjects walking inside of a lower-body exoskeleton. ROIAL learns Bayesian posteriors that predict each exoskeleton user’s utility landscape across four exoskeleton gait parameters. The algorithm discovers both commonalities and discrepancies across users’ gait preferences and identifies the gait parameters that most influenced user feedback. These results demonstrate the feasibility of recovering gait utility landscapes from limited human trials.

Many estimation problems in robotics, computer vision, and learning require estimating unknown quantities in the face of outliers. Outliers are typically the result of incorrect data association or feature matching, and it is not uncommon to have problems where more than 90% of the measurements used for estimation are outliers. While current approaches for robust estimation (e.g., RANSAC or graduated non-convexity) are able to deal with moderate amounts of outliers, they fail to produce accurate estimates in the presence of many outliers. This paper develops an approach to prune outliers. First, we develop a theory of invariance that allows us to quickly check if a subset of measurements are mutually compatible without explicitly solving the corresponding estimation problem. Second, we develop a graph-theoretic framework, where measurements are modeled as vertices and mutual compatibility is captured by edges in a graph. We generalize existing results showing that the inliers form a clique in this compatibility graph and typically belong to the maximum clique. We also show that in practice the maximum k-core of the compatibility graph provides an approximation of the maximum clique, while being much faster to compute in large problems. The combination of these two contributions leads to ROBIN, our approach to Reject Outliers Based on INvariants, which allows us to quickly prune outliers in generic estimation problems. We demonstrate ROBIN in four geometric perception problems and show it boosts robustness of existing solvers (making them robust to more than 95% outliers), while running in milliseconds in large problems.

We transform classical robot inertial parameter identification into an online learning problem by integrating state-of-the-art gradient descent techniques and first-order principles from mechanics and differential geometry. Through this, incremental learning of fully physically feasible inertial properties without requiring any prior information is made possible. This is achieved using a version of Riemannian gradient descent equipped with experience replay that guarantees feasible parameter updates at all times during learning. Analysis of the method's performance are done on a virtual manipulator focusing on the influence that different measurement setups have on the estimation as well as on parameter feasibility and re-learning. Finally, we present experimental results on a real 7 DoF manipulator and evaluate the quality of the generated inverse dynamics torques and the corresponding model error.

This work proposes a RGB-D SLAM system specifically designed for structured environments and aimed at improved tracking and mapping accuracy by relying on geometric features that are extracted from the surrounding. Structured environments offer, in addition to points, also an abundance of geometrical features such as lines and planes, which we exploit to design both the tracking and mapping components of our SLAM system. For the tracking part, we explore geometric relationships between these features based on the assumption of a Manhattan World (MW). We propose a decoupling-refinement method based on points, lines, and planes, as well as the use of Manhattan relationships in an additional pose refinement module. For the mapping part, different levels of maps from sparse to dense are reconstructed at a low computational cost. We propose an instance-wise meshing strategy to build a dense map by meshing plane instances independently. The overall performance in terms of pose estimation and reconstruction is evaluated on public benchmarks and shows improved performance compared to state-of-the-art methods. The code is released at https://github.com/yanyan-li/PlanarSLAM.

General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most of the success grasps, or only take the unstable depth image or point cloud as input which may lead to poor results in some cases. In this paper, we propose RGBD-Grasp, a pipeline that solves this problem by decoupling 7-DoF grasp detection into two sub-tasks where RGB and depth information are processed separately. In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image. Consequently, a Fast Analytic Searching(FAS) module calculates the opening width and the distance of the gripper to the grasp point. By decoupling the grasp detection problem and introducing the stable RGB modality, our pipeline alleviates the requirement for the high-quality depth image and is robust to depth sensor noise. We achieve state-of-the-art results on GraspNet-1Billion dataset compared with several baselines. Real robot experiments on a UR5 robot with an Intel Realsense camera and a Robotiq two-finger gripper show high success rates for both single object scenes and cluttered scenes. Our code and trained model are available at graspnet.net.

Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments or lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal dataset collected in an off-road environment, which contains annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University, and presents challenges to existing algorithms related to class imbalance and environmental topography. Additionally, we evaluate the current state of the art deep learning semantic segmentation models on this dataset. Experimental results show that RELLIS-3D presents challenges for algorithms designed for segmentation in urban environments. This novel dataset provides the resources needed by researchers to continue to develop more advanced algorithms and investigate new research directions to enhance autonomous navigation in off-road environments. RELLIS-3D is available at https://github.com/unmannedlab/RELLIS-3D

Reliable robotic grasping in unstructured environments is a crucial but challenging task. The main problem is to generate the optimal grasp of novel objects from partial noisy observations. This paper presents an end-to-end grasp detection network taking one single-view point cloud as input to tackle the problem. Our network includes three stages: Score Network (SN), Grasp Region Network (GRN), and Refine Network (RN). Specifically, SN regresses point grasp confidence and selects positive points with high confidence. Then GRN conducts grasp proposal prediction on the selected positive points. RN generates more accurate grasps by refining proposals predicted by GRN. To further improve the performance, we propose a grasp anchor mechanism, in which grasp anchors with assigned gripper orientations are introduced to generate grasp proposals. Experiments demonstrate that REGNet achieves a success rate of 79.34% and a completion rate of 96% in real-world clutter, which significantly outperforms several state-of-the-art point-cloud based methods, including GPD, PointNetGPD, and S4G. The code is available at https://github.com/zhaobinglei/REGNet for 3D Grasping.

Ceramics are one of the major sources of information about the past for archaeologists, with a typical archaeological dig unearthing 1000’s of pottery fragments (sherds) each day. However, archaeologists often are not allowed to remove these sherds from their home countries. Therefore, logging data (e.g., mass, color, decoration) in the field is the only way to record valuable information about these sherds. Currently, this laborious process is done manually, using up much of the valuable time at a dig site. This project aims to automate the data collection process, freeing up archaeologists to spend their limited time in the field on other tasks, and to create a large-scale digital database of sherd information, allowing archaeologists to take advantage of new computational tools to make discoveries. The contribution of this paper is an automated system, consisting of several reconfigurable data collection stations and a robotic arm to transport objects between stations, that can rapidly generate a large database of archaeological artifacts. We validate our system in simulation, using high-resolution models of sherds. In other contexts, our system may help to expand the use of automation in materials handling, parts sorting, and more.

Datasets for autonomous cars are essential for the development and benchmarking of perception systems. However, most existing datasets are captured with camera and LiDAR sensors in good weather conditions. In this paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to facilitate research on object detection, tracking and scene understanding using radar sensing for safe autonomous driving. RADIATE includes 3 hours of annotated radar images with more than 200K labelled road actors in total, on average about 4.6 instances per radar image. It covers 8 different categories of actors in a variety of weather conditions (e.g., sun, night, rain, fog and snow) and driving scenarios (e.g., parked, urban, motorway and suburban), representing different levels of challenge. To the best of our knowledge, this is the first public radar dataset which provides high-resolution radar images on public roads with a large amount of road actors labelled. The data collected in adverse weathers, e.g., fog and snowfall, is unique. Some baseline results of radar based object detection and recognition are given to show that the use of radar data is promising for automotive applications in bad weather, where vision and LiDAR fail. RADIATE also has stereo images, 32-channel LiDAR and GPS data, directed at other applications such as sensor fusion, localisation and mapping. The public dataset can be accessed at http://pro.hw.ac.uk/radiate/.

The purpose of the present work is the reconstruction of motorcycle lateral dynamics. The main idea is to estimate pertinent states and unknown inputs (rider action) with respect to nonlinear outputs due to motion transformation frames (inertial sensors are away from the local frame). To overcome this issue, we propose a new Unknown Input Observers with variable output matrix. In this paper, we take into account the ground truth measurements provided in the body-fixed frame, parametric uncertainties as well as sensors noise. This step leads to a nonlinear parameter-dependent output equation with unmeasured premise variables in the observer design. The observer synthesis is specified in term of convergence and stability study by considering a quadratic Lyapunov function associated with the Input To State Stability (ISS) property. Sufficient conditions are agreed in terms of Linear Matrix Inequalities (LMIs). Finally, the performances, usefulness and robustness of the proposed approach are assessed throughout an electric Scooter under urban riding scenario.

The robotic joint redundancy for executing a task and the optimal usage of robotic joints given the redundant degrees of freedom are crucial for the performance of a robot. It is therefore of interest to quantify the joint redundancy to better understand the robotic dexterity considering the dynamic feasibility. To this end, model-based approaches have been among the most commonly used methods to quantify the joint redundancy of simple robots analytically. However, this classical approach fails when applied to non-conventional complex robots. In this study, we propose a new method based on a deep reinforcement learning-derived metric, the synergy exploration area (SEA) metric, for the quantification of redundancy with a given dynamic environment. We conducted various experiments with different robotic structures for different tasks, ranging from simple robotic arm manipulation to more complex robotic locomotion. The experimental results show that the SEA metric can effectively quantify the relative joint redundancy over different robotic structures with varying degrees of freedom under unknown dynamic situations.

Object pose estimation enables robots to understand and interact with their environments. Training with synthetic data is necessary in order to adapt to novel situations. Unfortunately, pose estimation under domain shift, i.e., training on synthetic data and testing in the real world, is challenging. Deep learning-based approaches currently perform best when using encoder-decoder networks but typically do not generalize to new scenarios with different scene characteristics. We argue that patch-based approaches, instead of encoder-decoder networks, are more suited for synthetic-to-real transfer because local to global object information is better represented. To that end, we present a novel approach based on a specialized feature pyramid network to compute multi-scale features for creating pose hypotheses on different feature map resolutions in parallel. Our single-shot pose estimation approach is evaluated on multiple standard datasets and outperforms the state of the art by up to ∼35 %. We also perform grasping experiments in the real world to demonstrate the advantage of using synthetic data to generalize to novel environments.

We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code changes.To reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://github.com/erdos-project/pylot.

With the increased availability of rich tactile sensors, there is an an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch – the first machine learning library dedicated to the processing of touch sensing signals. PyTouch, is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service with the goal of unifying the tactile sensing community by providing a library for building scalable, proven, and performance-validated modules over which applications and research can be built upon. We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations. PyTouch is open-sourced at https://github.com/facebookresearch/pytouch.