OpenRaiser/Intern-Atlas huggingface dataset:
Active Exploration and Mapping via Iterative Covariance Regulation over Continuous SE(3) Trajectories
Venue: Conference IROS 2021
Year: 2021

This paper develops \emph{iterative Covariance Regulation} (iCR), a novel method for active exploration and mapping for a mobile robot equipped with on-board sensors. The problem is posed as optimal control over the $SE(3)$ pose kinematics of the robot to minimize the differential entropy of the map conditioned the potential sensor observations. We introduce a differentiable field of view formulation, and derive iCR via the gradient descent method to iteratively update an open-loop control sequence in continuous space so that the covariance of the map estimate is minimized. We demonstrate autonomous exploration and uncertainty reduction in simulated occupancy grid environments.
Adaptive Hyperparameter Tuning for Black-box LiDAR Odometry
Venue: Conference IROS 2021
Year: 2021

This study proposes an adaptive data-driven hyperparameter tuning framework for black-box 3D LiDAR odometry algorithms. The proposed framework comprises offline parameter-error function modeling and online adaptive parameter selection. In the offline step, we run the odometry estimation algorithm for tuning with different parameters and environments and evaluate the accuracy of the estimated trajectories to build a surrogate function that predicts the trajectory estimation error for the given parameters and environments. Subsequently, we select the parameter set that is expected to result in good accuracy in the given environment based on trajectory error prediction with the surrogate function. The proposed framework does not require detailed information on the inner working of the algorithm to be tuned, and improves its accuracy by adaptively optimizing the parameter set. We first demonstrate the role of the proposed framework in improving the accuracy of odometry estimation across different environments with a simulation-based toy example. Further, an evaluation on the public dataset KITTI shows that the proposed framework can improve the accuracy of several odometry estimation algorithms in practical situations.
Improving Grasp Stability with Rotation Measurement from Tactile Sensing
Venue: Conference IROS 2021
Year: 2021

Rotational displacement about the grasping point is a common grasp failure when an object is grasped at a location away from its center of gravity. Tactile sensors with soft surfaces, such as GelSight sensors, can detect the rotation patterns on the contacting surfaces when the object rotates. In this work, we propose a model-based algorithm that detects those rotational patterns and measures rotational displacement using the GelSight sensor. We also integrate the rotation detection feedback into a closed-loop regrasping framework, which detects the rotational failure of grasp in an early stage and drives the robot to a stable grasp pose. We validate our proposed rotation detection algorithm and grasp-regrasp system on self-collected dataset and online experiments to show how our approach accurately detects the rotation and increases grasp stability.
3D Ensemble-Based Online Oceanic Flow Field Estimation for Underwater Glider Path Planning
Venue: Conference IROS 2021
Year: 2021

Estimating ocean flow fields in 3D is a critical step in enabling the reliable operation of underwater gliders and other small, low-powered autonomous marine vehicles. Existing methods produce depth-averaged 2D layers arranged at discrete vertical intervals, but this type of estimation can lead to severe navigation errors. Based on the observation that real-world ocean currents exhibit relatively low velocity vertical components, we propose an accurate 3D estimator that extends our previous work in estimating 2D flow fields as a linear combination of basis flows. The proposed algorithm uses data from ensemble forecasting to build a set of 3D basis flows, and then iteratively updates basis coefficients using point measurements of underwater currents. We report results from experiments using actual ensemble forecasts and synthetic measurements to compare the performance of our method to the direct 3D extension of the previous work. These results show that our method produces estimates with dramatically lower error metrics, with and without measurement noise.
Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm
Venue: Conference IROS 2021
Year: 2021

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures. Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system. AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems. This approach generally involves running many simulations, which can be very expensive when using a high-fidelity simulator. To improve efficiency, we present a method that first finds failures in a low-fidelity simulator. It then uses the backward algorithm, which trains a deep neural network policy using a single expert demonstration, to adapt the low-fidelity failures to high-fidelity. We have created a series of autonomous vehicle validation case studies that represent some of the ways low-fidelity and high-fidelity simulators can differ, such as time discretization. We demonstrate in a variety of case studies that this new AST approach is able to find failures with significantly fewer high-fidelity simulation steps than are needed when just running AST directly in high-fidelity. As a proof of concept, we also demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art high-fidelity simulator for finding failures in autonomous vehicles.
Learning Linear Policies for Robust Bipedal Locomotion on Terrains with Varying Slopes
Venue: Conference IROS 2021
Year: 2021

In this paper, with a view toward deployment of light-weight control frameworks for bipedal walking robots, we realize end-foot trajectories that are shaped by a single linear feedback policy. We learn this policy via a model-free and a gradient-free learning algorithm, Augmented Random Search (ARS), in the two robot platforms Rabbit and Digit. Our contributions are two-fold: a) By using torso and support plane orientation as inputs, we achieve robust walking on slopes of up to 20 degrees in simulation. b) We demonstrate additional behaviors like walking backwards, stepping-in-place, and recovery from external pushes of up to 120 N. The end result is a robust and a fast feedback control law for bipedal walking on terrains with varying slopes. Towards the end, we also provide preliminary results of hardware transfer to Digit.
Joint Space Control via Deep Reinforcement Learning
Venue: Conference IROS 2021
Year: 2021

The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics. We propose a simple, versatile joint-level controller that dispenses with differential equations entirely. A deep neural network, trained via model-free reinforcement learning, is used to map from task space to joint space. Experiments show the method capable of achieving similar error to traditional methods, while greatly simplifying the process by automatically handling redundancy, joint limits, and acceleration / deceleration profiles. The basic technique is extended to avoid obstacles by augmenting the input to the network with information about the nearest obstacles. Results are shown both in simulation and on a real robot via sim-to-real transfer of the learned policy. We show that it is possible to achieve sub-centimeter accuracy, both in simulation and the real world, with a moderate amount of training.
Geometry-based Graph Pruning for Lifelong SLAM
Venue: Conference IROS 2021
Year: 2021

Lifelong SLAM considers long-term operation of a robot where already mapped locations are revisited many times in changing environments. As a result, traditional graph-based SLAM approaches eventually become extremely slow due to the continuous growth of the graph and the loss of sparsity. Both problems can be addressed by a graph pruning algorithm. It carefully removes vertices and edges to keep the graph size reasonable while preserving the information needed to provide good SLAM results. We propose a novel method that considers geometric criteria for choosing the vertices to be pruned. It is efficient, easy to implement, and leads to a graph with evenly spread vertices that remain part of the robot trajectory. Furthermore, we present a novel approach of marginalization that is more robust to wrong loop closures than existing methods. The proposed algorithm is evaluated on two publicly available real-world long-term datasets and compared to the unpruned case as well as ground truth. We show that even on a long dataset (25h), our approach manages to keep the graph sparse and the speed high while still providing good accuracy (40 times speed up, 6cm map error compared to unpruned case).
Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments
Venue: Conference IROS 2021
Year: 2021

In the context of autonomous vehicles, one of the most crucial tasks is to estimate the risk of the undertaken action. While navigating in complex urban environments, the Bayesian occupancy grid is one of the most popular types of maps, where the information of occupancy is stored as the probability of collision. Although widely used, this kind of representation is not well suited for risk assessment: because of its discrete nature, the probability of collision becomes dependent on the tessellation size. Therefore, risk assessments on Bayesian occupancy grids cannot yield risks with meaningful physical units. In this article, we propose an alternative framework called Dynamic Lambda-Field that is able to assess generic physical risks in dynamic environments without being dependent on the tessellation size. Using our framework, we are able to plan safe trajectories where the risk function can be adjusted depending on the scenario. We validate our approach with quantitative experiments, showing the convergence speed of the grid and that the framework is suitable for real-world scenarios.
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting
Venue: Conference IROS 2021
Year: 2021

Robust real-time detection and motion forecasting of traffic participants is necessary for autonomous vehicles to safely navigate urban environments. In this paper, we present RV-FuseNet, a novel end-to-end approach for joint detection and trajectory estimation directly from time-series LiDAR data. Instead of the widely used bird's eye view (BEV) representation, we utilize the native range view (RV) representation of LiDAR data. The RV preserves the full resolution of the sensor by avoiding the voxelization used in the BEV. Furthermore, RV can be processed efficiently due to its compactness. Previous approaches project time-series data to a common viewpoint for temporal fusion, and often this viewpoint is different from where it was captured. This is sufficient for BEV methods, but for RV methods, this can lead to loss of information and data distortion which has an adverse impact on performance. To address this challenge we propose a simple yet effective novel architecture, \textit{Incremental Fusion}, that minimizes the information loss by sequentially projecting each RV sweep into the viewpoint of the next sweep in time. We show that our approach significantly improves motion forecasting performance over the existing state-of-the-art. Furthermore, we demonstrate that our sequential fusion approach is superior to alternative RV based fusion methods on multiple datasets.
Attention Augmented ConvLSTM for Environment Prediction
Venue: Conference IROS 2021
Year: 2021

Safe and proactive planning in robotic systems generally requires accurate predictions of the environment. Prior work on environment prediction applied video frame prediction techniques to bird's-eye view environment representations, such as occupancy grids. ConvLSTM-based frameworks used previously often result in significant blurring and vanishing of moving objects, thus hindering their applicability for use in safety-critical applications. In this work, we propose two extensions to the ConvLSTM to address these issues. We present the Temporal Attention Augmented ConvLSTM (TAAConvLSTM) and Self-Attention Augmented ConvLSTM (SAAConvLSTM) frameworks for spatiotemporal occupancy prediction, and demonstrate improved performance over baseline architectures on the real-world KITTI and Waymo datasets.
Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
Venue: Conference IROS 2021
Year: 2021

Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose-only demonstrations and thus only skills with spatial and temporal features. In this work, we extend the LfD framework to address forceful manipulation skills, which are of great importance for industrial processes such as assembly. For such skills, multi-modal demonstrations including robot end-effector poses, force and torque readings, and operation scene are essential. Our objective is to reproduce such skills reliably according to the demonstrated pose and force profiles within different scenes. The proposed method combines our previous work on task-parameterized optimization and attractor-based impedance control. The learned skill model consists of (i) the attractor model that unifies the pose and force features, and (ii) the stiffness model that optimizes the stiffness for different stages of the skill. Furthermore, an online execution algorithm is proposed to adapt the skill execution to real-time observations of robot poses, measured forces, and changed scenes. We validate this method rigorously on a 7-DoF robot arm over several steps of an E-bike motor assembly process, which require different types of forceful interaction such as insertion, sliding and twisting.
REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs
Venue: Conference IROS 2021
Year: 2021

Exploring an unknown environment without colliding with obstacles is one of the essentials of autonomous vehicles to perform diverse missions such as structural inspections, rescues, deliveries, and so forth. Therefore, unmanned aerial vehicles (UAVs), which are fast, agile, and have high degrees of freedom, have been widely used. However, previous approaches have two limitations: a) First, they may not be appropriate for exploring large-scale environments because they mainly depend on random sampling-based path planning that causes unnecessary movements. b) Second, they assume the pose estimation is accurate enough, which is the most critical factor in obtaining an accurate map. In this paper, to explore and map unknown large-scale environments rapidly and accurately, we propose a novel exploration method that combines the pre-calculated Peacock Trajectory with graph-based global exploration and active loop-closing. Because the two-step trajectory that considers the kinodynamics of UAVs is used, obstacle avoidance is guaranteed in the receding-horizon manner. In addition, local exploration that considers the frontier and global exploration based on the graph maximizes the speed of exploration by minimizing unnecessary revisiting. In addition, by actively closing the loop based on the likelihood, pose estimation performance is improved. The proposed method's performance is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.
Real-Time Motion Planning of a Hydraulic Excavator using Trajectory Optimization and Model Predictive Control
Venue: Conference IROS 2021
Year: 2021

Automation of excavation tasks requires real-time trajectory planning satisfying various constraints. To guarantee both constraint feasibility and real-time trajectory re-plannability, we present an integrated framework for real-time optimization-based trajectory planning of a hydraulic excavator. The proposed framework is composed of two main modules: a global planner and a real-time local planner. The global planner computes the entire global trajectory considering excavation volume and energy minimization while the local counterpart tracks the global trajectory in a receding horizon manner, satisfying dynamic feasibility, physical constraints, and disturbance-awareness. We validate the proposed planning algorithm in a simulation environment where two types of operations are conducted in the presence of emulated disturbance from hydraulic friction and soil-bucket interaction: shallow and deep excavation. The optimized global trajectories are obtained in an order of a second, which is tracked by the local planner at faster than 30 Hz. To the best of our knowledge, this work presents the first real-time motion planning framework that satisfies constraints of a hydraulic excavator, such as force/torque, power, cylinder displacement, and flow rate limits.
Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning
Venue: Conference IROS 2021
Year: 2021

Recently, needs for unmanned aerial vehicles (UAVs) that are attachable to the wall have been highlighted. As one of the ways to address the need, researches on various tilting multirotors that can increase maneuverability has been employed. Unfortunately, existing studies on the tilting multirotors require considerable amounts of prior information on the complex dynamic model. Meanwhile, reinforcement learning on quadrotors has been studied to mitigate this issue. Yet, these are only been applied to standard quadrotors, whose systems are less complex than those of tilting multirotors. In this paper, a novel reinforcement learning-based method is proposed to control a tilting multirotor on real-world applications, which is the first attempt to apply reinforcement learning to a tilting multirotor. To do so, we propose a novel reward function for a neural network model that takes power efficiency into account. The model is initially trained over a simulated environment and then fine-tuned using real-world data in order to overcome the sim-to-real gap issue. Furthermore, a novel, efficient state representation with respect to the goal frame that helps the network learn optimal policy better is proposed. As verified on real-world experiments, our proposed method shows robust controllability by overcoming the complex dynamics of tilting multirotors.
Probabilistic Iterative LQR for Short Time Horizon MPC
Venue: Conference IROS 2021
Year: 2021

Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another controller. In this work, we instead consider trajectory distribution as the solution of an optimal control problem, resulting in better tracking performance and a more stable controller. A Gaussian distribution is first obtained from an iterative Linear Quadratic Regulator (iLQR) solver. A short horizon Model Predictive Control (MPC) is then used to track this distribution. We show that tracking the distribution is more cost-efficient and robust as compared to tracking the mean or using iLQR feedback control. The proposed method is validated with kinematic control of 7-DoF Panda manipulator and dynamic control of 6-DoF quadcopter in simulation.
Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation
Venue: Conference IROS 2021
Year: 2021

Grasping in cluttered scenes has always been a great challenge for robots, due to the requirement of the ability to well understand the scene and object information. Previous works usually assume that the geometry information of the objects is available, or utilize a step-wise, multi-stage strategy to predict the feasible 6-DoF grasp poses. In this work, we propose to formalize the 6-DoF grasp pose estimation as a simultaneous multi-task learning problem. In a unified framework, we jointly predict the feasible 6-DoF grasp poses, instance semantic segmentation, and collision information. The whole framework is jointly optimized and end-to-end differentiable. Our model is evaluated on large-scale benchmarks as well as the real robot system. On the public dataset, our method outperforms prior state-of-the-art methods by a large margin (+4.08 AP). We also demonstrate the implementation of our model on a real robotic platform and show that the robot can accurately grasp target objects in cluttered scenarios with a high success rate. Project link: https://openbyterobotics.github.io/sscl
Learning to Design and Construct Bridge without Blueprint
Venue: Conference IROS 2021
Year: 2021

Autonomous assembly has been a desired functionality of many intelligent robot systems. We study a new challenging assembly task, designing and constructing a bridge without a blueprint. In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design. In this paper, we propose a bi-level approach to tackle this task. At the high level, the system learns a bridge blueprint policy in a physical simulator using deep reinforcement learning and curriculum learning. A policy is represented as an attention-based neural network with object-centric input, which enables generalization to different numbers of blocks and cliff widths. For low-level control, we implement a motion-planning-based policy for real-robot motion control, which can be directly combined with a trained blueprint policy for real-world bridge construction without tuning. In our field study, our bi-level robot system demonstrates the capability of manipulating blocks to construct a diverse set of bridges with different architectures.
SSC: Semantic Scan Context for Large-Scale Place Recognition
Venue: Conference IROS 2021
Year: 2021

Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, normal, reflection intensity, etc., as local or global descriptors to represent scenes. Besides, they often ignore the translation between point clouds when matching descriptors. Different from most existing methods, we explore the use of high-level features, namely semantics, to improve the descriptor's representation ability. Also, when matching descriptors, we try to correct the translation between point clouds to improve accuracy. Concretely, we propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance. Our experiments on the KITTI dataset show that our approach outperforms the state-of-the-art methods with a large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.