215 research outputs found
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
This paper presents two variations of a novel stochastic prediction algorithm
that enables mobile robots to accurately and robustly predict the future state
of complex dynamic scenes. The proposed algorithm uses a variational
autoencoder to predict a range of possible future states of the environment.
The algorithm takes full advantage of the motion of the robot itself, the
motion of dynamic objects, and the geometry of static objects in the scene to
improve prediction accuracy. Three simulated and real-world datasets collected
by different robot models are used to demonstrate that the proposed algorithm
is able to achieve more accurate and robust prediction performance than other
prediction algorithms. Furthermore, a predictive uncertainty-aware planner is
proposed to demonstrate the effectiveness of the proposed predictor in
simulation and real-world navigation experiments. Implementations are open
source at https://github.com/TempleRAIL/SOGMP.Comment: Accepted by 7th Annual Conference on Robot Learning (CoRL), 202
Data ethics : building trust : how digital technologies can serve humanity
Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century:
For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car,
from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad,
for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world?
How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations
in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication.
The authors and institutions come from all continents.
The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust!
The book is a continuation of the volume โCyber Ethics 4.0โ published in 2018 by the same editors
์ค์ ํํ๋ ํ๊ฒฝ์์ Look-ahead Point๋ฅผ ์ด์ฉํ ๋ชจ๋ฐฉํ์ต ๊ธฐ๋ฐ ์์จ ๋ด๋น๊ฒ์ด์ ๋ฐฉ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(์ง๋ฅํ์ตํฉ์์คํ
์ ๊ณต), 2023. 2. ๋ฐ์ฌํฅ.๋ณธ ํ์๋
ผ๋ฌธ์ ์์จ์ฃผํ ์ฐจ๋์ด ์ฃผ์ฐจ์ฅ์์ ์์์ง๋์ ๋น์ ์ผ์๋ก ๋ด๋น๊ฒ์ด์
์ ์ํํ๋ ๋ฐฉ๋ฒ๋ค์ ์ ์ํฉ๋๋ค. ์ด ํ๊ฒฝ์์์ ์์จ์ฃผํ ๊ธฐ์ ์ ์์ ์์จ์ฃผํ์ ์์ฑํ๋ ๋ฐ ํ์ํ๋ฉฐ, ํธ๋ฆฌํ๊ฒ ์ด์ฉ๋ ์ ์์ต๋๋ค. ์ด ๊ธฐ์ ์ ๊ตฌํํ๊ธฐ ์ํด, ๊ฒฝ๋ก๋ฅผ ์์ฑํ๊ณ ์ด๋ฅผ ํ์งํ ๋ฐ์ดํฐ๋ก ์ถ์ข
ํ๋ ๋ฐฉ๋ฒ์ด ์ผ๋ฐ์ ์ผ๋ก ์ฐ๊ตฌ๋๊ณ ์์ต๋๋ค. ๊ทธ๋ฌ๋, ์ฃผ์ฐจ์ฅ์์๋ ๋๋ก ๊ฐ ๊ฐ๊ฒฉ์ด ์ข๊ณ ์ฅ์ ๋ฌผ์ด ๋ณต์กํ๊ฒ ๋ถํฌ๋์ด ์์ด ํ์งํ ๋ฐ์ดํฐ๋ฅผ ์ ํํ๊ฒ ์ป๊ธฐ ํ๋ญ๋๋ค. ์ด๋ ์ค์ ๊ฒฝ๋ก์ ์ถ์ข
ํ๋ ๊ฒฝ๋ก ์ฌ์ด์ ํ์ด์ง์ ๋ฐ์์์ผ, ์ฐจ๋๊ณผ ์ฅ์ ๋ฌผ ๊ฐ ์ถฉ๋ ๊ฐ๋ฅ์ฑ์ ๋์
๋๋ค. ๋ฐ๋ผ์ ํ์งํ ๋ฐ์ดํฐ๋ก ๊ฒฝ๋ก๋ฅผ ์ถ์ข
ํ๋ ๋์ , ๋ฎ์ ๋น์ฉ์ ๊ฐ์ง๋ ๋น์ ์ผ์๋ก ์ฐจ๋์ด ์ฃผํ ๊ฐ๋ฅ ์์ญ์ ํฅํด ์ฃผํํ๋ ๋ฐฉ๋ฒ์ด ์ ์๋ฉ๋๋ค.
์ฃผ์ฐจ์ฅ์๋ ์ฐจ์ ์ด ์๊ณ ๋ค์ํ ์ ์ /๋์ ์ฅ์ ๋ฌผ์ด ๋ณต์กํ๊ฒ ์์ด, ์ฃผํ ๊ฐ๋ฅ/๋ถ๊ฐ๋ฅํ ์์ญ์ ๊ตฌ๋ถํ์ฌ ์ ์ ๊ฒฉ์ ์ง๋๋ฅผ ์ป๋ ๊ฒ์ด ํ์ํฉ๋๋ค. ๋ํ, ๊ต์ฐจ๋ก๋ฅผ ๋ด๋น๊ฒ์ด์
ํ๊ธฐ ์ํด, ์ ์ญ ๊ณํ์ ๋ฐ๋ฅธ ํ๋์ ๊ฐ๋ ๋๋ก๋ง์ด ์ฃผํ๊ฐ๋ฅ ์์ญ์ผ๋ก ๊ตฌ๋ถ๋ฉ๋๋ค. ๊ฐ๋ ๋๋ก๋ ํ์ ๋ ๋ฐ์ด๋ฉ ๋ฐ์ค ํํ๋ก ์ธ์๋๋ฉฐ ์ฃผํ๊ฐ๋ฅ ์์ญ ์ธ์๊ณผ ํจ๊ป multi-task ๋คํธ์ํฌ๋ฅผ ํตํด ์ป์ด์ง๋๋ค. ์ฃผํ์ ์ํด ๋ชจ๋ฐฉํ์ต์ด ์ฌ์ฉ๋๋ฉฐ, ์ด๋ ๋ชจ๋ธ-๊ธฐ๋ฐ ๋ชจ์
ํ๋๋ ๋ฐฉ๋ฒ๋ณด๋ค ํ๋ผ๋ฏธํฐ ํ๋ ์์ด๋ ๋ค์ํ๊ณ ๋ณต์กํ ํ๊ฒฝ์ ๋ค๋ฃฐ ์ ์๊ณ ๋ถ์ ํํ ์ธ์ ๊ฒฐ๊ณผ์๋ ๊ฐ์ธํฉ๋๋ค. ์์ธ๋ฌ, ์ด๋ฏธ์ง์์ ์ ์ด ๋ช
๋ น์ ๊ตฌํ๋ ๊ธฐ์กด ๋ชจ๋ฐฉํ์ต ๋ฐฉ๋ฒ๊ณผ ๋ฌ๋ฆฌ, ์ ์ ๊ฒฉ์ ์ง๋์์ ์ฐจ๋์ด ๋๋ฌํ look-ahead point๋ฅผ ํ์ตํ๋ ์๋ก์ด ๋ชจ๋ฐฉํ์ต ๋ฐฉ๋ฒ์ด ์ ์๋ฉ๋๋ค. ์ด point๋ฅผ ์ฌ์ฉํจ์ผ๋ก์จ, ๋ชจ๋ฐฉ ํ์ต์ ์ฑ๋ฅ์ ํฅ์์ํค๋ data aggregation (DAgger) ์๊ณ ๋ฆฌ์ฆ์ ๋ณ๋์ ์กฐ์ด์คํฑ ์์ด ์์จ์ฃผํ์ ์ ์ฉํ ์ ์์ผ๋ฉฐ, ์ ๋ฌธ๊ฐ๋ human-in-loop DAgger ํ๋ จ ๊ณผ์ ์์๋ ์ต์ ์ ํ๋์ ์ ์ ํํ ์ ์์ต๋๋ค. ์ถ๊ฐ๋ก, DAgger ๋ณํ ์๊ณ ๋ฆฌ์ฆ๋ค์ ์์ ํ์ง ์๊ฑฐ๋ ์ถฉ๋์ ๊ฐ๊น์ด ์ํฉ์ ๋ํ ๋ฐ์ดํฐ๋ฅผ ์ํ๋งํ์ฌ DAgger ์ฑ๋ฅ์ด ํฅ์๋ฉ๋๋ค. ๊ทธ๋ฌ๋, ์ ์ฒด ํ๋ จ ๋ฐ์ดํฐ์
์์ ์ด ์ํฉ์ ๋ํ ๋ฐ์ดํฐ ๋น์จ์ด ์ ์ผ๋ฉด, ์ถ๊ฐ์ ์ธ DAgger ์ํ ๋ฐ ์ฌ๋์ ๋
ธ๋ ฅ์ด ์๊ตฌ๋ฉ๋๋ค. ์ด ๋ฌธ์ ๋ฅผ ๋ค๋ฃจ๊ธฐ ์ํด, ๊ฐ์ค ์์ค ํจ์๋ฅผ ์ฌ์ฉํ๋ ์๋ก์ด DAgger ํ๋ จ ๋ฐฉ๋ฒ์ธ WeightDAgger ์๊ณ ๋ฆฌ์ฆ์ด ์ ์๋๋ฉฐ, ๋ ์ ์ DAgger ๋ฐ๋ณต์ผ๋ก ์์ ์ธ๊ธ ๊ฒ๊ณผ ์ ์ฌํ ์ํฉ์์ ์ ๋ฌธ๊ฐ์ ํ๋์ ๋ ์ ํํ๊ฒ ๋ชจ๋ฐฉํ ์ ์์ต๋๋ค. DAgger๋ฅผ ๋์ ์ํฉ๊น์ง ํ์ฅํ๊ธฐ ์ํด, ์์ด์ ํธ์ ๊ฒฝ์ํ๋ ์ ๋์ ์ ์ฑ
์ด ์ ์๋๊ณ , ์ด ์ ์ฑ
์ DAgger ์๊ณ ๋ฆฌ์ฆ์ ์ ์ฉํ๊ธฐ ์ํ ํ๋ จ ํ๋ ์์ํฌ๊ฐ ์ ์๋ฉ๋๋ค. ์์ด์ ํธ๋ ์ด์ DAgger ํ๋ จ ๋จ๊ณ์์ ํ๋ จ๋์ง ์์ ๋ค์ํ ์ํฉ์ ๋ํด ํ๋ จ๋ ์ ์์ ๋ฟ๋ง ์๋๋ผ ์ฌ์ด ์ํฉ์์ ์ด๋ ค์ด ์ํฉ๊น์ง ์ ์ง์ ์ผ๋ก ํ๋ จ๋ ์ ์์ต๋๋ค.
์ค๋ด์ธ ์ฃผ์ฐจ์ฅ์์์ ์ฐจ๋ ๋ด๋น๊ฒ์ด์
์คํ์ ํตํด, ๋ชจ๋ธ-๊ธฐ๋ฐ ๋ชจ์
ํ๋๋ ์๊ณ ๋ฆฌ์ฆ์ ํ๊ณ ๋ฐ ์ด๋ฅผ ๋ค๋ฃฐ ์ ์๋ ์ ์ํ๋ ๋ชจ๋ฐฉํ์ต ๋ฐฉ๋ฒ์ ํจ์ฉ์ฑ์ด ๋ถ์๋ฉ๋๋ค. ๋ํ, ์๋ฎฌ๋ ์ด์
์คํ์ ํตํด, ์ ์๋ WeightDAgger๊ฐ ๊ธฐ์กด DAgger ์๊ณ ๋ฆฌ์ฆ๋ค ๋ณด๋ค ๋ ์ ์ DAgger ์ํ ๋ฐ ์ฌ๋์ ๋
ธ๋ ฅ์ด ํ์ํจ์ ๋ณด์ด๋ฉฐ, ์ ๋์ ์ ์ฑ
์ ์ด์ฉํ DAgger ํ๋ จ ๋ฐฉ๋ฒ์ผ๋ก ๋์ ์ฅ์ ๋ฌผ์ ์์ ํ๊ฒ ํํผํ ์ ์์์ ๋ณด์
๋๋ค. ์ถ๊ฐ์ ์ผ๋ก, ๋ถ๋ก์์๋ ๋น์ ๊ธฐ๋ฐ ์์จ ์ฃผ์ฐจ ์์คํ
๋ฐ ์ฃผ์ฐจ ๊ฒฝ๋ก๋ฅผ ๋น ๋ฅด๊ฒ ์์ฑํ ์ ์๋ ๋ฐฉ๋ฒ์ด ์๊ฐ๋์ด, ๋น์ ๊ธฐ๋ฐ ์ฃผํ ๋ฐ ์ฃผ์ฐจ๋ฅผ ์ํํ๋ ์์จ ๋ฐ๋ ํํน ์์คํ
์ด ์์ฑ๋ฉ๋๋ค.This thesis proposes methods for performing autonomous navigation with a topological map and a vision sensor in a parking lot. These methods are necessary to complete fully autonomous driving and can be conveniently used by humans. To implement them, a method of generating a path and tracking it with localization data is commonly studied. However, in such environments, the localization data is inaccurate because the distance between roads is narrow, and obstacles are distributed complexly, which increases the possibility of collisions between the vehicle and obstacles. Therefore, instead of tracking the path with the localization data, a method is proposed in which the vehicle drives toward a drivable area obtained by vision having a low-cost.
In the parking lot, there are complicated various static/dynamic obstacles and no lanes, so it is necessary to obtain an occupancy grid map by segmenting the drivable/non-drivable areas. To navigating intersections, one branch road according to a global plan is configured as the drivable area. The branch road is detected in a shape of a rotated bounding box and is obtained through a multi-task network that simultaneously recognizes the drivable area. For driving, imitation learning is used, which can handle various and complex environments without parameter tuning and is more robust to handling an inaccurate perception result than model-based motion-planning algorithms. In addition, unlike existing imitation learning methods that obtain control commands from an image, a new imitation learning method is proposed that learns a look-ahead point that a vehicle will reach on an occupancy grid map. By using this point, the data aggregation (DAgger) algorithm that improves the performance of imitation learning can be applied to autonomous navigating without a separate joystick, and the expert can select the optimal action well even in the human-in-loop DAgger training process. Additionally, DAgger variant algorithms improve DAgger's performance by sampling data for unsafe or near-collision situations. However, if the data ratio for these situations in the entire training dataset is small, additional DAgger iteration and human effort are required. To deal with this problem, a new DAgger training method using a weighted loss function (WeightDAgger) is proposed, which can more accurately imitate the expert action in the aforementioned situations with fewer DAgger iterations. To extend DAgger to dynamic situations, an adversarial agent policy competing with the agent is proposed, and a training framework to apply this policy to DAgger is suggested. The agent can be trained for a variety of situations not trained in previous DAgger training steps, as well as progressively trained from easy to difficult situations.
Through vehicle navigation experiments in real indoor and outdoor parking lots, limitations of the model-based motion-planning algorithms and the effectiveness of the proposed method to deal with them are analyzed. Besides, it is shown that the proposed WeightDAgger requires less DAgger performance and human effort than the existing DAgger algorithms, and the vehicle can safely avoid dynamic obstacles with the DAgger training framework using the adversarial agent policy. Additionally, the appendix introduces a vision-based autonomous parking system and a method to quickly generate the parking path, completing the vision-based autonomous valet parking system that performs driving as well as parking.1 INTRODUCTION 1
1.1 Autonomous Driving System and Environments 1
1.2 Motivation 4
1.3 Contributions of Thesis 6
1.4 Overview of Thesis 8
2 MULTI-TASK PERCEPTION NETWORK FOR VISION-BASED NAVIGATION 9
2.1 Introduction 9
2.1.1 Related Works 10
2.2 Proposed Method 13
2.2.1 Bird's-Eye-View Image Transform 14
2.2.2 Multi-Task Perception Network 15
2.2.2.1 Drivable Area Segmentation (Occupancy Grid Map (OGM)) 16
2.2.2.2 Rotated Road Bounding Box Detection 18
2.2.3 Intersection Decision 21
2.2.3.1 Road Occupancy Grid Map (OGMroad) 22
2.2.4 Merged Occupancy Grid Map (OGMmer) 23
2.3 Experiment 25
2.3.1 Experimental Setup 25
2.3.1.1 Autonomous Vehicle 25
2.3.1.2 Multi-task Network Setup 27
2.3.1.3 Model-based Branch Road Detection Method 29
2.3.2 Experimental Results 30
2.3.2.1 Quantitative Analysis of Multi-Task Network 30
2.3.2.2 Comparison of Branch Road Detection Method 31
2.4 Conclusion 34
3 DATA AGGREGATION (DAGGER) ALGORITHM WITH LOOK-AHEAD POINT FOR AUTONOMOUS DRIVING IN SEMI-STRUCTURED ENVIRONMENT 35
3.1 Introduction 35
3.2 Related Works & Background 41
3.2.1 DAgger Algorithms for Autonomous Driving 41
3.2.2 Behavior Cloning 42
3.2.3 DAgger Algorithm 43
3.3 Proposed Method 45
3.3.1 DAgger with Look-ahead Point Composition (State & Action) 45
3.3.2 Loss Function 49
3.3.3 Data-sampling Function in DAgger 50
3.3.4 Reasons to Use Look-ahead Point As Action 52
3.4 Experimental Setup 54
3.4.1 Driving Policy Network Training 54
3.4.2 Model-based Motion-Planning Algorithms 56
3.5 Experimental Result 57
3.5.1 Quantitative Analysis of Driving Policy 58
3.5.1.1 Collision Rate 58
3.5.1.2 Safe Distance Range Ratio 59
3.5.2 Qualitative Analysis of Driving Policy 60
3.5.2.1 Limitations of Tentacle Algorithm 60
3.5.2.2 Limitations of VVF Algorithm 61
3.5.2.3 Limitations of Both Tentacle and VVF 62
3.5.2.4 Driving Results on Noisy Occupancy Grid Map 63
3.5.2.5 Intersection Navigation 65
3.6 Conclusion 68
4 WEIGHT DAGGER ALGORITHM FOR REDUCING IMITATION LEARNING ITERATIONS 70
4.1 Introduction 70
4.2 Related Works & Background 71
4.3 Proposed Method 74
4.3.1 Weighted Loss Function in WeightDAgger 75
4.3.2 Weight Update Process in Entire Training Dataset 78
4.4 Experiments 80
4.4.1 Experimental Setup 80
4.4.2 Experimental Results 82
4.4.2.1 Ablation Study According to ฯ 82
4.4.2.2 Ablation Study According to ฮต 83
4.4.2.3 Ablation Study According to ฮฑ 84
4.4.2.4 Driving Test Results 85
4.4.3 Walking Robot Experiments 86
4.5 Conclusion 87
5 DAGGER USING ADVERSARIAL AGENT POLICY FOR DYNAMIC SITUATIONS 89
5.1 Introduction 89
5.2 Related Works & Background 91
5.2.1 Motion-planning Algorithms for Dynamic Situations 91
5.2.2 DAgger Algorithm for Dynamic Situation 93
5.3 Proposed Method 95
5.3.1 DAgger Training Framework Using Adversarial Agent Policy 95
5.3.2 Applying to Oncoming Dynamic Obstacle Avoidance Task 97
5.3.2.1 Ego Agent Policy 98
5.3.2.2 Adversarial Agent Policy 100
5.4 Experiments 101
5.4.1 Experimental Setup 101
5.4.1.1 Ego Agent Policy Training 102
5.4.1.2 Adversarial Agent Policy Training 103
5.4.2 Experimental Result 103
5.4.2.1 Performance of Adversarial Agent Policy 103
5.4.2.2 Ego Agent Policy Performance Comparisons Trained with / without Adversarial Agent Policy 104
5.5 Conclusion 106
6 CONCLUSIONS 107
Appendix A 110
A.1 Vision-based Re-plannable Autonomous Parking System 110
A.1.1 Parking Spot Detection 112
A.1.2 Re-planning Method 113
A.2 Biased Target-tree* with RRT* Algorithm for Fast Parking Path Planning 115
A.2.1 Introduction 115
A.2.2 Proposed Method 117
A.2.3 Experiments 119
Abstract (In Korean) 143
Acknowledgement 145๋ฐ
Structured machine learning models for robustness against different factors of variability in robot control
An important feature of human sensorimotor skill is our ability to learn to reuse them across different environmental contexts, in part due to our understanding of attributes of variability in these environments. This thesis explores how the structure of models used within learning for robot control could similarly help autonomous robots cope with variability, hence achieving skill generalisation. The overarching approach is to develop modular architectures that judiciously combine different forms of inductive bias for learning. In particular, we consider how models and policies should be structured in order to achieve robust behaviour in the face of different factors of variation - in the environment, in objects and in other internal parameters of a policy - with the end goal of more robust, accurate and data-efficient skill acquisition and adaptation.
At a high level, variability in skill is determined by variations in constraints presented by the external environment, and in task-specific perturbations that affect the specification of optimal action. A typical example of environmental perturbation would be variation in lighting and illumination, affecting the noise characteristics of perception. An example of task perturbations would be variation in object geometry, mass or friction, and in the specification of costs associated with speed or smoothness of execution. We counteract these factors of variation by exploring three forms of structuring: utilising separate data sets curated according to the relevant factor of variation, building neural network models that incorporate this factorisation into the very structure of the networks, and learning structured loss functions. The thesis is comprised of four projects exploring this theme within robotics planning and prediction tasks.
Firstly, in the setting of trajectory prediction in crowded scenes, we explore a modular architecture for learning static and dynamic environmental structure. We show that factorising the prediction problem from the individual representations allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments. This modularity explicitly allows for a more flexible and interpretable adaptation of trajectory prediction models to using
pre-trained state of the art models. We show that this results in more efficient motion prediction and allows for performance comparable to the state-of-the-art supervised 2D trajectory prediction.
Next, in the domain of contact-rich robotic manipulation, we consider a modular architecture that combines model-free learning from demonstration, in particular dynamic movement primitives (DMP), with modern model-free reinforcement learning (RL), using both on-policy and off-policy approaches. We show that factorising the skill learning problem to skill acquisition and error correction through policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. Our empirical evaluation demonstrates how to best do this with DMPs and propose โresidual Learning from Demonstrationโ (rLfD), a framework that combines DMPs with RL to learn a residual correction policy. Our evaluations, performed both in simulation and on a physical system, suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs. Last but not least, our study shows that the extracted correction policies can be transferred to different geometries and frictions through few-shot task adaptation.
Third, we employ meta learning to learn time-invariant reward functions, wherein both the objectives of a task (i.e., the reward functions) and the policy for performing that task optimally are learnt simultaneously. We propose a novel inverse reinforcement learning (IRL) formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks.
Finally, we employ our observations to evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies under which adversarial defences are successfully retained, in addition to revealing potential vulnerabilities. We study the extent to which adversarially robust features can preserve their defence properties against black and white-box attacks under three different transfer learning strategies. Our empirical evaluations give insights on how well adversarial robustness under transfer learning can generalise.
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications
L'abstract รจ presente nell'allegato / the abstract is in the attachmen
Targeted Learning: A Hybrid Approach to Social Robot Navigation
Empowering robots to navigate in a socially compliant manner is essential for
the acceptance of robots moving in human-inhabited environments. Previously,
roboticists have developed classical navigation systems with decades of
empirical validation to achieve safety and efficiency. However, the many
complex factors of social compliance make classical navigation systems hard to
adapt to social situations, where no amount of tuning enables them to be both
safe (people are too unpredictable) and efficient (the frozen robot problem).
With recent advances in deep learning approaches, the common reaction has been
to entirely discard classical navigation systems and start from scratch,
building a completely new learning-based social navigation planner. In this
work, we find that this reaction is unnecessarily extreme: using a large-scale
real-world social navigation dataset, SCAND, we find that classical systems can
be used safely and efficiently in a large number of social situations (up to
80%). We therefore ask if we can rethink this problem by leveraging the
advantages of both classical and learning-based approaches. We propose a hybrid
strategy in which we learn to switch between a classical geometric planner and
a data-driven method. Our experiments on both SCAND and two physical robots
show that the hybrid planner can achieve better social compliance in terms of a
variety of metrics, compared to using either the classical or learning-based
approach alone
University of Windsor Undergraduate Calendar 2023 Spring
https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1023/thumbnail.jp
People flor maps for socially conscious robot navigation
With robots becoming increasingly common in human occupied spaces, there has been a growing body of research into the problem of socially conscious robot navigation. A robot must be able to predict and anticipate the movements of people around it in order to navigate in a way that is socially acceptable, or it may face rejection and therefore failure. Often this motion prediction is achieved using neural networks or artificial intelligence to predict the trajectories or flow of people, requiring large amounts of expensive and time-consuming real-world data collection. Therefore, many recent studies have attempted to find a way to create simulated human trajectory data. A variety of methods have been used to achieve this, the main ones being path planning algorithms and pedestrian simulators, but no study has evaluated these methods against each other and real-world data. This thesis compares the ability of two path planning algorithms (A* and RRT*) and a pedestrian simulator (PTV Vissim) to make realistic maps of dynamics. It concludes that A*-based path planners are the best choice when balancing the ability to replicate realistic people flow with the ease of generating large amounts of data
ATHENA Research Book, Volume 2
ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orlรฉans, University of Siegen, Hellenic Mediterranean University, Niccolรฒ Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skลodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers
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