| 1. |
- Almeida, Tiago, 1996-, et al.
(författare)
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THÖR-Magni : Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction
- 2023
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Ingår i: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). - : IEEE. - 9798350307450 - 9798350307443 ; , s. 2192-2201
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Konferensbidrag (refereegranskat)abstract
- Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.
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| 2. |
- Brucker, Manuel, et al.
(författare)
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Semantic Labeling of Indoor Environments from 3D RGB Maps
- 2018
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Ingår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE Computer Society. - 9781538630815 ; , s. 1871-1878
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Konferensbidrag (refereegranskat)abstract
- We present an approach to automatically assign semantic labels to rooms reconstructed from 3D RGB maps of apartments. Evidence for the room types is generated using state-of-the-art deep-learning techniques for scene classification and object detection based on automatically generated virtual RGB views, as well as from a geometric analysis of the map's 3D structure. The evidence is merged in a conditional random field, using statistics mined from different datasets of indoor environments. We evaluate our approach qualitatively and quantitatively and compare it to related methods.
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| 3. |
- Heiden, Eric, et al.
(författare)
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Bench-MR : A Motion Planning Benchmark for Wheeled Mobile Robots
- 2021
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 6:3, s. 4536-4543
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Tidskriftsartikel (refereegranskat)abstract
- Planning smooth and energy-efficient paths for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, several sampling-based motion-planning algorithms, extend functions and post-smoothing algorithms have been introduced for such motion-planning systems. Choosing the best combination of components for an application is a tedious exercise, even for expert users. We therefore present Bench-MR, the first open-source motion-planning benchmarking framework designed for sampling-based motion planning for nonholonomic, wheeled mobile robots. Unlike related software suites, Bench-MR is an easy-to-use and comprehensive benchmarking framework that provides a large variety of sampling-based motion-planning algorithms, extend functions, collision checkers, post-smoothing algorithms and optimization criteria. It aids practitioners and researchers in designing, testing, and evaluating motion-planning systems, and comparing them against the state of the art on complex navigation scenarios through many performance metrics. Through several experiments, we demonstrate how Bench-MR can be used to gain extensive insights from the benchmarking results it generates.
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| 4. |
- Heuer, Lukas, 1992-, et al.
(författare)
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Proactive Model Predictive Control with Multi-Modal Human Motion Prediction in Cluttered Dynamic Environments
- 2023
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Ingår i: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA. - : IEEE. - 9781665491914 - 9781665491907 ; , s. 229-236
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Konferensbidrag (refereegranskat)abstract
- For robots navigating in dynamic environments, exploiting and understanding uncertain human motion prediction is key to generate efficient, safe and legible actions. The robot may perform poorly and cause hindrances if it does not reason over possible, multi-modal future social interactions. With the goal of enhancing autonomous navigation in cluttered environments, we propose a novel formulation for nonlinear model predictive control including multi-modal predictions of human motion. As a result, our approach leads to less conservative, smooth and intuitive human-aware navigation with reduced risk of collisions, and shows a good balance between task efficiency, collision avoidance and human comfort. To show its effectiveness, we compare our approach against the state of the art in crowded simulated environments, and with real-world human motion data from the THOR dataset. This comparison shows that we are able to improve task efficiency, keep a larger distance to humans and significantly reduce the collision time, when navigating in cluttered dynamic environ-ments. Furthermore, the method is shown to work robustly with different state-of-the-art human motion predictors.
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| 5. |
- Luber, Matthias, et al.
(författare)
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People tracking with human motion predictions from social forces
- 2010
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Ingår i: 2010 IEEE International Conference on Robotics and Automation, Proceedings. - : IEEE conference proceedings. ; , s. 464-469
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Konferensbidrag (refereegranskat)abstract
- For many tasks in populated environments, robots need to keep track of current and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity or acceleration. But even over a short period, human behavior is more complex and influenced by factors such as the intended goal, other people, objects in the environment, and social rules. This motivates the use of more sophisticated motion models for people tracking especially since humans frequently undergo lengthy occlusion events. In this paper, we consider computational models developed in the cognitive and social science communities that describe individual and collective pedestrian dynamics for tasks such as crowd behavior analysis. In particular, we integrate a model based on a social force concept into a multi-hypothesis target tracker. We show how the refined motion predictions translate into more informed probability distributions over hypotheses and finally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range finder, the social force model leads to more accurate tracking with up to two times fewer data association errors.
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| 6. |
- Molina, Sergi, et al.
(författare)
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The ILIAD Safety Stack : Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots
- 2023
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Ingår i: IEEE robotics & automation magazine. - : IEEE. - 1070-9932 .- 1558-223X.
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Tidskriftsartikel (refereegranskat)abstract
- Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.
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| 7. |
- Palmieri, Luigi, et al.
(författare)
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Dispertio : Optimal Sampling For Safe Deterministic Motion Planning
- 2020
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 5:2, s. 362-368
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Tidskriftsartikel (refereegranskat)abstract
- A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.
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| 8. |
- Palmieri, Luigi, et al.
(författare)
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Guest Editorial : Introduction to the Special Issue on Long-Term Human Motion Prediction
- 2021
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE Press. - 2377-3766. ; 6:3, s. 5613-5617
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundamental for robust long-term operation of robotic systems and safe human-robot collaboration. Foreseeing how a scene with multiple agents evolves over time and incorporating predictions in a proactive manner allows for novel ways of planning and control, active perception, or humanrobot interaction. Recent planning and control approaches use predictive techniques to better cope with the dynamics of the environment, thus allowing the generation of smoother and more legible robot motion. Predictions can be provided as input to the planning or optimization algorithm (e.g. as a cost term or heuristic function), or as additional dimension to consider in the problem formulation (leading to an increased computational complexity). Recent perception techniques deeply interconnect prediction modules with detection, segmentation and tracking, to generally increase the accuracy of different inference tasks, i.e. filtering, predicting. As also indicated by some of the scientific works accepted in this special issue, novel deep learning architectures allow better interleaving of the aforementioned units.
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| 9. |
- Rudenko, Andrey, 1991-, et al.
(författare)
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Human Motion Prediction under Social Grouping Constraints
- 2018
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Ingår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. - 9781538680940 - 9781538680957 ; , s. 3358-3364
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Konferensbidrag (refereegranskat)abstract
- Accurate long-term prediction of human motion inpopulated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety offactors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics.
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| 10. |
- Rudenko, Andrey, 1991-, et al.
(författare)
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Human motion trajectory prediction : a survey
- 2020
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 39:8, s. 895-935
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Tidskriftsartikel (refereegranskat)abstract
- With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.
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