Distributed robotic systems rely heavily on publish-subscribe frameworks, such as ROS, to efficiently implement modular computation graphs. The ROS 2 executor, a high-level task scheduler which handles messages internally, is a performance bottleneck. In previous work, we presented ros2_tracing, a framework with instrumentation and tools for real-time tracing of ROS 2. We now extend on that instrumentation and leverage the tracing tools to propose an analysis and visualization of the flow of messages across distributed ROS 2 systems. Our proposed method detects one-to-many and many-to-many causal links between input and output messages, including indirect causal links through simple user-level annotations. We validate our method on both synthetic and real robotic systems, and demonstrate its low runtime overhead. Moreover, the underlying intermediate execution representation database can be further leveraged to extract additional metrics and high-level results. This can provide valuable timing and scheduling information to further study and improve the ROS 2 executor as well as optimize any ROS 2 system. The source code is available at: https://github.com/christophebedard/ros2-message-flow-analysis.
Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multi-modality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions using complex fusion mechanisms. In this respect, we propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. Although this backbone already provides promising results, we demonstrate that its prediction accuracy can be improved considerably when combined with a scene-aware goal-estimation module. To this end, we employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations. We conduct extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY) and show that our approach performs on par with state-of-the-art techniques while reducing model complexity.
Vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treating each cell as a token. However, not all regions are equally important in human-centric vision tasks, e.g., the human body needs a fine representation with many tokens, while the image background can be modeled by a few tokens. To address this problem, we propose a novel Vision Transformer, called Token Clustering Transformer (TCFormer), which merges tokens by progressive clustering, where the tokens can be merged from different locations with flexible shapes and sizes. The tokens in TCFormer can not only focus on important areas but also adjust the token shapes to fit the semantic concept and adopt a fine resolution for regions containing critical details, which is beneficial to capturing detailed information. Extensive experiments show that TCFormer consistently outperforms its counterparts on different challenging human-centric tasks and datasets, including whole-body pose estimation on COCO-WholeBody and 3D human mesh reconstruction on 3DPW. Code is available at https://github.com/zengwang430521/TCFormer.git
The separation of training and data transmission as well as the frequent uplink/downlink (UL/DL) switching make time-division duplex (TDD)-based massive multiple-input multiple-output (mMIMO) systems less competent in fast time-varying scenarios due to the resulted severe channel aging. To this end, a multicarrier-division duplex (MDD) mMIMO scheme associated with two types of well-designed frame structures are introduced for combating channel aging when communicating over fast time-varying channels. To compare with TDD, the corresponding frame structures related to 3GPP standards and their variant forms are presented. The MDD-specific general Wiener predictor and decision-directed Wiener predictor are introduced to predict the channel state information, respectively, in the time domain based on UL pilots and in the frequency domain based on the detected UL data, considering the impact of residual self-interference (SI). Moreover, by applying the zero-forcing precoding and maximum ratio combining, the closed-form approximations for the lower bounded rate achieved by TDD and MDD systems over time-varying channels are derived. Our main conclusion from this study is that the MDD, endowed with the capability of full-duplex but less demand on SI cancellation than in-band full-duplex (IBFD), outperforms both the conventional TDD and IBFD in combating channel aging.
Voice Conversion(VC) refers to changing the timbre of a speech while retaining the discourse content. Recently, many works have focused on disentangle-based learning techniques to separate the timbre and the linguistic content information from a speech signal. Once successful, voice conversion will be feasible and straightforward. This paper proposed a novel one-shot voice conversion framework based on vector quantization voice conversion (VQVC) and AutoVC, called AVQVC. A new training method is applied to VQVC to separate content and timbre information from speech more effectively. The result shows that this approach has better performance than VQVC in separating content and timbre to improve the sound quality of generated speech.
Representation and control of the dynamics of stigmergic substances used by bio-inspired approaches is a challenge when applied to robotics. In order to overcome this challenge, this work proposes a model to coordinate swarms of robots based on the virtualisation and control of these substances in a local scope. The model presents a new pheromone modelling, which enables the decentralisation and asynchronicity of navigation decisions. Each robot maintains an independent virtual pheromone map, which is continuously updated with the robot's deposits and pheromone evaporation. Moreover, the individual pheromone map is also updated by aggregating information from other robots that are exploring nearby areas. Thus, individual and independent maps replace the need of a centralising agent that controls and distributes the pheromone information, which is not always practicable. Pheromone information propagation is inspired by ants' vibroacoustic communication, which, in turn, is characterised as an indirect communication through a type of gossip protocol. The proposed model was evaluated through an agent simulation software, implemented by the authors, and in the Webots platform. Experiments were carried out to validate the model in different environments, with different shapes and sizes, as well as varying the number of robots. The analysis of the results has shown that the model was able to perform the coordination of the swarm, and the robots have exhibited an expressive performance executing the surveillance task.
Multi-view image acquisition systems with two or more cameras can be rather costly due to the number of high resolution image sensors that are required. Recently, it has been shown that by covering a low resolution sensor with a non-regular sampling mask and by using an efficient algorithm for image reconstruction, a high resolution image can be obtained. In this paper, a stereo image reconstruction setup for multi-view scenarios is proposed. A scene is captured by a pair of non-regular sampling sensors and by incorporating information from the adjacent view, the reconstruction quality can be increased. Compared to a state-of-the-art single-view reconstruction algorithm, this leads to a visually noticeable average gain in PSNR of 0.74 dB.
We present a novel framework for motion forecasting with Dual Consistency Constraints and Multi-Pseudo-Target supervision. The motion forecasting task predicts future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of DCMS is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during the training stage. In addition, we design a novel self-ensembling scheme to obtain accurate pseudo targets to model the multi-modality in motion forecasting through supervision with multiple targets explicitly, namely Multi-Pseudo-Target supervision. Our experimental results on the Argoverse motion forecasting benchmark show that DCMS significantly outperforms the state-of-the-art methods, achieving 1st place on the leaderboard. We also demonstrate that our proposed strategies can be incorporated into other motion forecasting approaches as general training schemes.
The emerging field of fluidically actuated soft robotic control has promising capabilities such as inherent compliance and user safety. However, these are counterbalanced by issues not common to rigid robots, like nonlinear actuation dynamics, motion constraints, workspace limitations, and variable shape stiffness. In this work, we have adapted Model Predictive Control (MPC), that has recently seen an exponential rise in popularity and fields of applications, to a soft robotic arm called SoPrA. We have addressed the problems that current control methods are facing, trying to propose a unique environment to handle them in a modular way. This work shows, both with simulation and experimental results, that Task-Space MPC can be successfully implemented for dynamic soft robotic control, while past research has usually focused on Joint-Space references. We have provided a way to couple the Piece-wise Constant Curvature and the Augmented Rigid Body Model assumptions with internal and external constraints and actuation dynamics, delivering an algorithm that can manage all these information and optimize over them. We believe that an MPC implementation based on our approach could be the way to address most of model-based soft robotics control issues within a unified and modular framework, while allowing to include improvements that usually belong to other control domains such as learning techniques.
Spatio-temporal graphs (ST-graphs) have been used to model time series tasks such as traffic forecasting, human motion modeling, and action recognition. The high-level structure and corresponding features from ST-graphs have led to improved performance over traditional architectures. However, current methods tend to be limited by simple features, despite the rich information provided by the full graph structure, which leads to inefficiencies and suboptimal performance in downstream tasks. We propose the use of features derived from meta-paths, walks across different types of edges, in ST-graphs to improve the performance of Structural Recurrent Neural Network. In this paper, we present the Meta-path Enhanced Structural Recurrent Neural Network (MESRNN), a generic framework that can be applied to any spatio-temporal task in a simple and scalable manner. We employ MESRNN for pedestrian trajectory prediction, utilizing these meta-path based features to capture the relationships between the trajectories of pedestrians at different points in time and space. We compare our MESRNN against state-of-the-art ST-graph methods on standard datasets to show the performance boost provided by meta-path information. The proposed model consistently outperforms the baselines in trajectory prediction over long time horizons by over 32\%, and produces more socially compliant trajectories in dense crowds. For more information please refer to the project website at https://sites.google.com/illinois.edu/mesrnn/home.