The deployment of multi-stream fusion strategy on behavioral recognition from skeletal data can extract complementary features from different information streams and improve the recognition accuracy, but suffers from high model complexity and a large number of parameters. Besides, existing multi-stream methods using a fixed adjacency matrix homogenizes the model’s discrimination process across diverse actions, causing reduction of the actual lift for the multi-stream model. Finally, attention mechanisms are commonly applied to the multi-dimensional features, including spatial, temporal and channel dimensions. But their attention scores are typically fused in a concatenated manner, leading to the ignorance of the interrelation between joints in complex actions. To alleviate these issues, the Front-Rear dual Fusion Graph Convolutional Network (FRF-GCN) is proposed to provide a lightweight model based on skeletal data. Targeted adjacency matrices are also designed for different front fusion streams, allowing the model to focus on actions of varying magnitudes. Simultaneously, the mechanism of Spatial-Temporal-Channel Parallel Attention (STC-P), which processes attention in parallel and places greater emphasis on useful information, is proposed to further improve model’s performance. FRF-GCN demonstrates significant competitiveness compared to the current state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120 and Kinetics-Skeleton 400 datasets. Our code is available at: https://github.com/sunbeam-kkt/FRF-GCN-master.

Obtaining ground truth annotations for 3D pose estimation (3D HPE) typically depends on motion capture equipment (Mocap), which is not only expensive but impractical for widespread deployment. In contrast, triangulation can reconstruct 3D poses solely from multi-view 2D poses with known camera parameters, eliminating the need for Mocap. However, inherent noise in 2D pose predictions introduces uncertainties, compromising the reliability of the results. To obtain more reliable annotations with noisy input, we introduce an annotation approach for the 3D HPE task, driven by prior knowledge of the skeletal configuration. We split our approach into two steps: first a parametric model is designed to enhance confidence predictions. Then, a differentiable weighted triangulation is employed to estimate the 3D pose in world space, leveraging the predicted confidence scores as weights. The pipeline is trained using a bone length loss. Moreover, we collect a multi-view dataset for 3D HPE and annotate it using our proposed annotation tool. This dataset is characterized by more construction scenarios, including heavier occlusion cases, diverse viewing directions, and the integration of various optical sensors, setting it apart from existing datasets. Experiments on both our dataset and Human3.6M demonstrate the effectiveness of our method.

The current learning pipelines for robotics manipulation infer movement primitives sequentially along the temporal-evolving axis, which can result in an accumulation of prediction errors and subsequently cause the visual observations to fall out of the training distribution. This paper proposes a novel hierarchical behavior cloning approach which tries to dissociate standard behaviour cloning (BC) pipeline to two stages. The intuition of this approach is to eliminate accumu-lation errors using a fixed spatial representation. At first stage, a high-level planner will be employed to translate the initial observation of the scene into task-specific spatial waypoints. Then, a low-level robotic path planner takes over the task of guiding the robot by executing a set of pre-defined elementary movements or actions known as primitives, with the goal of reaching the previously predicted waypoints. Our hierarchical keypoints-based paradigm aims to simplify existing temporal-evolving approach to a more simple way: directly spatialize the whole sequential primitives as a set of 8D waypoints only from the very first observation. Plentiful experiments demon-strate that our paradigm can achieve comparable results with Reinforcement Learning (RL) and outperforms existing offline BC approaches, with only a single-shot inference from the initial observation. Code and models are available at: https://github.com/KevinRiou22/spatial-fixing-il

3D points acquisitions based on robust sensors such as tactile or laser sensors are true alternatives to computer vision for 3D object recognition. In real life scenarios where robots are equipped with such sensors to acquire 3D data, only few points can be iteratively collected in a reasonable amount of time. However, existing Point-cloud classifiers are extremely sensitive to sparse points, missing parts and noise. To compensate for the sparsity of the data, some Reinforcement Learning (RL) based approaches have been proposed to learn a sparse yet efficient exploration of the target object regarding the 3D recognition objective. However, existing RL approaches only focus on classification performances to guide the training of the active acquisition-and-classification frameworks, and thus fail to dissociate poor exploration strategy (missing parts, noisy points) from actual classifier mistakes on proper data. In this study, we proposed a new RL framework that was rewarded regarding both the classification performances and the exploration quality. Our trained framework outperforms existing State-Of-The-Art models on 3D geometric objects classification. We further showed that our trained framework learnt to alternate between (1) a clean and broad exploration strategy, suitable for easily distinguishable categories, and (2) a specific local exploration strategy, facilitating the discrimination of similar categories.

For collaborative robotics applications, many systems have addressed general handling issues in recent years. Depending on the industrial context, these systems bring physical and cognitive feelings that result in the acceptance of their users. Transparency qualifies the ability of a robot to follow the movements imposed by the operator without noting any resistant effort. The objective of this article is to develop a methodology that mixes the approach highlighting the human factors and their correlation to robotic criteria in the case of a passive manipulation arm with six degrees of freedom produced by the Neoditech company. An exploratory study was then conducted to measure criteria such as time, speed, and effort during manipulation along with criteria based on a technological level, personality and technology acceptance model-method questionnaires. From there, we uncovered a correlation between the user’s personality, particularly their lack of neuroticism, and their means of evaluating the device through its usefulness, comfort, and indicators of mechanical behavior. This study is a preliminary analysis of user behaviors and traits that affect technological acceptance when dealing with a comanipulation arm. This work offers a framework for similar future analyses and recommends mechanical adjustments to the arm for increased user acceptance.

In order to be able to perform complex and arduous tasks, stiffness articular identification of industrial robots is a current approach to predict the deflection under static or dynamic loading. Manufacturers propose new features to take the loading into account and a new generation of industrial robot equiped with double encoding systems are proposed. However, current methods brings some drawbacks when the ratio between the stiffness arm and the wrist one is too high. In this paper, we propose a new approach to take this aspect into account by decoupling the arm identification and the wrist one. We compare then our method regarding two current methods and applied it on this new industrial robot. The results highlight the stability and the quality of the stiffness articular estimation with and without activating the double encoding system. On our data, we are able to take into account 84% of the global deflection.