ICRA 2024 论文列表

Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyzing intricate sensor data, and making decisions within split seconds to ensure safety through numerous computer vision tasks. In this paper, we present a new real-time multi-task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. To counter the challenge of negative transfer — the prevalent issue in multi-task learning — we introduce a task-adaptive attention generator. This generator is designed to automatically discern interrelations across the three tasks and arrange the task-sharing pattern, all while leveraging the efficiency of the hard-parameter sharing approach. To the best of our knowledge, the proposed model is pioneering in its capability to concurrently handle multiple tasks, notably 3D object detection, while maintaining real-time processing speeds. Our rigorously optimized network, when tested on the Cityscapes-3D datasets, consistently outperforms various base-line models. Moreover, an in-depth ablation study substantiates the efficacy of the methodologies integrated into our framework.

Efficient task allocation is a challenge for multirobot search. The multi-robot search problem is reformulated as submodular maximization subject to intersection system constraints. The objective function is submodular and consists of a coverage function to cover environments and a balancing function to efficiently dispatch robots. The intersection system is composed of routing and clustering constraints. The experiment results show that the proposed approach outperforms state-ofthe-art methods in multi-robot search.

In this work, we present a coordination strategy tailored for scenarios involving multiple agents and tasks. We devise a range of tasks using signal temporal logic (STL), each earmarked for specific agents. These tasks are then imposed through control barrier function (CBF) constraints to ensure completion. To extend existing methodologies, our framework adeptly manages interactions among multiple agents. This extension is facilitated by leveraging nonlinear model predictive control (NMPC) to compute trajectories that avoid collisions. An integral aspect of our approach is the integration of a human-in-the-loop (HIL) model. This model enables real-time integration of human directives into the coordination process. A novel task allocation protocol is embedded within the frame-work to guide this process. We substantiate our methodology through a series of experiments, which corroborate the viability and relevance of our algorithms.

Many applications of path planning in time-varying flow fields, particularly in areas such as marine robotics and ship routing, can be modelled as instances of the time-varying shortest path (TDSP) problem. Although there are no known polynomial-time solutions to TDSP in general, our recent work has identified a tractable case where the flow is modelled as piecewise constant. Extending this method to allow for computational reuse in larger multi-query problems, however, requires additional thought. This paper shows that the piecewise-linear form of the cost function employed in previously work can be used to build an analogy of a shortest path tree, thereby enabling optimal concatenation of sub-problem solutions in the absence of an optimal substructure, and without uniform time discretisation. We present a framework for multi-query TDSP that finds an optimal path that passes through a defined sequence of waypoints and is computationally efficient. Performance comparison is provided in simulation that shows large (up to 100x) speedup compared to a naive approach. This result is significant for applications such as ship routing, where route evaluation is a desirable capability.

Surgical robot task automation has been a promising research topic for improving surgical efficiency and quality. Learning-based methods have been recognized as an interesting paradigm and been increasingly investigated. However, existing approaches encounter difficulties in long-horizon goal-conditioned tasks due to the intricate compositional structure, which requires decision-making for a sequence of sub-steps and understanding of inherent dynamics of goal-reaching tasks. In this paper, we propose a new learning-based framework by leveraging the strong reasoning capability of the GPT-based architecture to automate surgical robotic tasks. The key to our approach is developing a goal-conditioned decision transformer to achieve sequential representations with goal-aware future indicators in order to enhance temporal reasoning. Moreover, considering to exploit a general understanding of dynamics inherent in manipulations, thus making the model’s reasoning ability to be task-agnostic, we also design a cross-task pretraining paradigm that uses multiple training objectives associated with data from diverse tasks. We have conducted extensive experiments on 10 tasks using the surgical robot learning simulator SurRoL [1]. The results show that our new approach achieves promising performance and task versatility compared to existing methods. The learned trajectories can be deployed on the da Vinci Research Kit (dVRK) for validating its practicality in real surgical robot settings. Our project website is at: https://med-air.github.io/SurRoL.

Springtails are tiny arthropods that crawl and jump. They jump by temporarily storing elastic energy in resilin elastic cuticular structures and releasing that energy to accelerate a tail, called a furca, propelling them in the air. This paper presents an autonomous, springtail-inspired microrobot that can crawl and jump. The microrobot has a mass of 980mg and stands 13mm tall, and has on-board sensing, computation, and power, enabling autonomy. The microrobot was designed with a super-elastic shape memory alloy (SMA) spring that is manually loaded to store elastic energy. The on-board sensing and computation triggers an actuator at the jump frequency range that unlatches the spring, launching the microrobot into the air at speeds up to 3.171ms−1. At the same time, the microrobot is capable of crawling, when actuated at frequencies lower or higher than the jump frequency range, demonstrating autonomous multi-modal locomotion. This work opens up new pathways toward autonomy in multi-modal microrobots.

Tracking a specific person in 3D scene is gaining momentum due to its numerous applications in robotics. Currently, most 3D trackers focus on driving scenarios with neglected jitter and uncomplicated surroundings, which results in their severe degeneration in complex environments, especially on jolting robot platforms (only 20-60% success rate). To improve the accuracy, a Point-Video-based Transformer Tracking model (PVTrack) is presented for robots. It is the first multi-modal 3D human tracking work that incorporates point clouds together with RGB videos to achieve information complementarity. Moreover, PVTrack proposes the Siamese Point-Video Transformer for feature aggregation to overcome dynamic environments, which captures more target-aware information through the hierarchical attention mechanism adaptively. Considering the violent shaking on robots and rugged terrains, a lateral Human-ware Proposal Network is designed together with an Anti-shake Proposal Compensation module. It alleviates the disturbance caused by complex scenes as well as the particularity of the robot platform. Experiments show that our method achieves state-of-the-art performance on both KITTI/Waymo datasets and a quadruped robot for various indoor and outdoor scenes.

Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling. Unlike existing research, which is limited to target segmentation, pose regression, or using fixed target blueprints, our work presents a holistic multi-level framework for part assembly planning consisting of part assembly sequence inference, part motion planning, and robot contact optimization. We present the Part Assembly Sequence Transformer (PAST) – a sequence-to-sequence neural network – to infer assembly sequences recursively from a target blueprint. We then use a motion planner and optimization to generate part movements and contacts. To train PAST, we introduce D4PAS: a large-scale Dataset for Part Assembly Sequences consisting of physically valid sequences for industrial objects. Experimental results show that our approach generalizes better than prior methods while needing significantly less computational time for inference. Further details on our experiments and results are available in the video.

Road pavement detection and segmentation are critical for developing autonomous road repair systems. However, developing an instance segmentation method that simultaneously performs multi-class defect detection and segmentation is challenging due to the textural simplicity of road pavement image, the diversity of defect geometries, and the morphological ambiguity between classes. We propose a novel end-to-end method for multi-class road defect detection and segmentation. The proposed method comprises multiple spatial and channel-wise attention blocks available to learn global representations across spatial and channel-wise dimensions. Through these attention blocks, more globally generalised representations of morphological information (spatial characteristics) of road defects and colour and depth information of images can be learned. To demonstrate the effectiveness of our framework, we conducted various ablation studies and comparisons with prior methods on a newly collected dataset annotated with nine road defect classes. The experiments show that our proposed method outperforms existing state-of-the-art methods for multi-class road defect detection and segmentation methods.

Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades. Simultaneously, recent parallel works in robotics have devised efficient algorithms for multi-agent path finding (MAPF), though often in environments with simplified kinematics. In this work, we hybridize insights and algorithms from MAPF with the structure and heuristics of optimizing the crossing order of CAVs at signal-free intersections. We devise an optimal and complete algorithm, Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which significantly outperforms existing algorithms, fixed heuristics, and prioritized planning with KATS. The performance is maintained under different vehicle arrival rates, lane lengths, crossing speeds, and control horizon. Through ablations and dissections, we offer insight on the contributing factors to OBS-KATS’s performance. Our work is directly applicable to many similarly scaled traffic and multi-robot scenarios with directed lanes.

Electric quadruped robots used in outdoor exploration are susceptible to leg-related electrical or mechanical failures. Unexpected joint power loss and joint locking can immediately pose a falling threat. Typically, controllers lack the capability to actively sense the condition of their own joints and take proactive actions. Maintaining the original motion patterns could lead to disastrous consequences, as the controller may produce irrational output within a short period of time, further creating the risk of serious physical injuries. This paper presents a hierarchical fault-tolerant control scheme employing a multi-task training architecture capable of actively perceiving and overcoming two types of leg joint faults. The architecture simultaneously trains three joint task policies for health, power loss, and locking scenarios in parallel, introducing a symmetric reflection initialization technique to ensure rapid and stable gait skill transformations. Experiments demonstrate that the control scheme is robust in unexpected scenarios where a single leg experiences concurrent joint faults in two joints. Furthermore, the policy retains the robot’s planar mobility, enabling rough velocity tracking. Finally, zero-shot Sim2Real transfer is achieved on the real-world SOLO8 robot, countering both electrical and mechanical failures.

We focus on the problem of long-range dynamic replanning for off-road autonomous vehicles, where a robot plans paths through a previously unobserved environment while continuously receiving noisy local observations. An effective approach for planning under sensing uncertainty is determinization, where one converts a stochastic world into a deterministic one and plans under this simplification. This makes the planning problem tractable, but the cost of following the planned path in the real world may be different than in the determinized world. This causes collisions if the determinized world optimistically ignores obstacles, or causes unnecessarily long routes if the determinized world pessimistically imagines more obstacles. We aim to be robust to uncertainty over potential worlds while still achieving the efficiency benefits of determinization. We evaluate algorithms for dynamic replanning on a large real-world dataset of challenging long-range planning problems from the DARPA RACER program. Our method, Dynamic Replanning via Evaluating and Aggregating Multiple Samples (DREAMS), outperforms other determinization-based approaches in terms of combined traversal time and collision cost. https://sites.google.com/cs.washington.edu/dreams/

Multi-robot systems are becoming increasingly prevalent in various real-world applications, such as manufacturing and warehouse logistics. These systems face complex challenges in 1) task allocation due to factors like time-extended tasks, and agent specialization, and 2) uncertainties in task execution. Potential task failures can add further contingency tasks to recover from the failure, thereby causing delays. This paper addresses the problem of Multi-Robot Task Allocation under Uncertainty by proposing a hierarchical approach that decouples the problem into two levels. We use a low-level optimization formulation to find the optimal solution for a deterministic multi-robot task allocation problem with known task outcomes. The higher-level search intelligently generates more likely combinations of failures and calls the inner-level search repeatedly to find the optimal task allocation sequence, given the known outcomes. We validate our results in simulation for a manufacturing domain and demonstrate that our method can reduce the effect of potential delays from contingencies. We show that our algorithm is computationally efficient while improving average makespan compared to other baselines.

This paper addresses multi-robot informative path planning (IPP) for environmental monitoring. The problem involves determining informative regions in the environment that should be visited by robots to gather the most information about the environment. We propose an efficient sparse Gaussian process-based approach that uses gradient descent to optimize paths in continuous environments. Our approach efficiently scales to both spatially and spatio-temporally correlated environments. Moreover, our approach can simultaneously optimize the informative paths while accounting for routing constraints, such as a distance budget and limits on the robot’s velocity and acceleration. Our approach can be used for IPP with both discrete and continuous sensing robots, with point and non-point field-of-view sensing shapes, and for both single and multi-robot IPP. We demonstrate that the proposed approach is fast and accurate on real-world data.

In public spaces shared with humans, ensuring multi-robot systems navigate without collisions while respecting social norms is challenging, particularly with limited communication. Although current robot social navigation techniques leverage advances in reinforcement learning and deep learning, they frequently overlook robot dynamics in simulations, leading to a simulation-to-reality gap. In this paper, we bridge this gap by presenting a new multi-robot social navigation environment crafted using Dec-POSMDP and multi-agent reinforcement learning. Furthermore, we introduce SAMARL: a novel benchmark for cooperative multi-robot social navigation. SAMARL employs a unique spatial-temporal transformer combined with multi-agent reinforcement learning. This approach effectively captures the complex interactions between robots and humans, thus promoting cooperative tendencies in multi-robot systems. Our extensive experiments reveal that SAMARL outperforms existing baseline and ablation models in our designed environment. Demo videos for this work can be found at: https://sites.google.com/view/samarl

In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory model that forecasts pedestrians’ trajectories. We formulate the local MPC formulation for each individual robot that includes both individual and shared objectives, in which the latter encourages the emergence of coordination among robots. Next, we consider the multi-robot navigation and human-robot interaction, respectively, as a potential game and a two-player game, then employ an iterative best response approach to solve the resulting optimization problems in a centralized and distributed fashion. Finally, we demonstrate the effectiveness of coordination among robots in simulated crowd navigation.

We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential out-comes of both a local robot’s and its neighbors’ next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm’s capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots.

This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion. While existing approaches have shown promising results in effectively extracting planar regions from the environment, they often suffer from issues such as low computational efficiency or loss of resolution. To address these issues, we propose a multi-resolution planar region extraction strategy in this paper that balances the accuracy in boundaries and computational efficiency. Our method begins with a pointwise classification preprocessing module, which categorizes all sampled points according to their local geometric properties to facilitate multi-resolution segmentation. Subsequently, we arrange the categorized points using an octree, followed by an in-depth analysis of nodes to finish multi-resolution plane segmentation. The efficiency and robustness of the proposed approach are verified via synthetic and real-world experiments, demonstrating our method’s ability to generalize effectively across various uneven terrains while maintaining real-time performance, achieving frame rates exceeding 35 FPS.

State estimation is a crucial component for the successful implementation of robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios, the performance of these sensors is degraded by challenging environments, e.g. adverse weather conditions and low-light scenarios. The emerging 4D imaging radar technology is capable of providing robust perception in adverse conditions. Despite its potential, challenges remain for indoor settings where noisy radar data does not present clear geometric features. Moreover, disparities in radar data resolution and field of view (FOV) can lead to inaccurate measurements. While prior research has explored radar-inertial odometry based on Doppler velocity information, challenges remain for the estimation of 3D motion because of the discrepancy in the FOV and resolution of the radar sensor. In this paper, we address Doppler velocity measurement uncertainties. We present a method to optimize body frame velocity while managing Doppler velocity uncertainty. Based on our observations, we propose a dual imaging radar configuration to mitigate the challenge of discrepancy in radar data. To attain high-precision 3D state estimation, we introduce a strategy that seamlessly integrates radar data with a consumer-grade IMU sensor using fixed-lag smoothing optimization. Finally, we evaluate our approach using real-world 3D motion data.