7,419 research outputs found
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of
robot programming by demonstration and semantic mapping. In SRP, a user can
directly program a robot manipulator by demonstrating a snapshot of their
intended goal scene in workspace. The robot then parses this goal as a scene
graph comprised of object poses and inter-object relations, assuming known
object geometries. Task and motion planning is then used to realize the user's
goal from an arbitrary initial scene configuration. Even when faced with
different initial scene configurations, SRP enables the robot to seamlessly
adapt to reach the user's demonstrated goal. For scene perception, we propose
the Discriminatively-Informed Generative Estimation of Scenes and Transforms
(DIGEST) method to infer the initial and goal states of the world from RGBD
images. The efficacy of SRP with DIGEST perception is demonstrated for the task
of tray-setting with a Michigan Progress Fetch robot. Scene perception and task
execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.Comment: published in ICRA 201
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν ν곡μ°μ£Όκ³΅νκ³Ό, 2020. 8. κΉνμ§.Learning from demonstrations (LfD) is a promising approach that enables robots to perform a specific movement. As robotic manipulations are substituting a variety of tasks, LfD algorithms are widely used and studied for specifying the robot configurations for the various types of movements.
This dissertation presents an approach based on parametric dynamic movement primitives (PDMP) as a motion representation algorithm which is one of relevant LfD techniques. Unlike existing motion representation algorithms, this work not only represents a prescribed motion but also computes the new behavior through a generalization of multiple demonstrations in the actual environment. The generalization process uses Gaussian process regression (GPR) by representing the nonlinear relationship between the PDMP parameters that determine motion and the corresponding environmental variables. The proposed algorithm shows that it serves as a powerful optimal and real-time motion planner among the existing planning algorithms when optimal demonstrations are provided as dataset.
In this dissertation, the safety of motion is also considered. Here, safety refers to keeping the system away from certain configurations that are unsafe. The safety criterion of the PDMP internal parameters are computed to check the safety. This safety criterion reflects the new behavior computed through the generalization process, as well as the individual motion safety of the demonstration set. The demonstrations causing unsafe movement are identified and removed. Also, the demolished demonstrations are replaced by proven demonstrations upon this criterion.
This work also presents an extension approach reducing the number of required demonstrations for the PDMP framework. This approach is effective where a single mission consists of multiple sub-tasks and requires numerous demonstrations in generalizing them. The whole trajectories in provided demonstrations are segmented into multiple sub-tasks representing unit motions. Then, multiple PDMPs are formed independently for correlated-segments. The phase-decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. GPR formulations are applied to obtain execution time and regional goal configuration for each sub-task.
Finally, the proposed approach and its extension are validated with the actual experiments of mobile manipulators. The first two scenarios regarding cooperative aerial transportation demonstrate the excellence of the proposed technique in terms of quick computation, generation of efficient movement, and safety assurance. The last scenario deals with two mobile manipulations using ground vehicles and shows the effectiveness of the proposed extension in executing complex missions.μμ° νμ΅ κΈ°λ²(Learning from demonstrations, LfD)μ λ‘λ΄μ΄ νΉμ λμμ μνν μ μλλ‘ νλ μ λ§ν λμ μμ± κΈ°λ²μ΄λ€. λ‘λ΄ μ‘°μκΈ°κ° μΈκ° μ¬νμμ λ€μν μ
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ν리머ν°λΈ κΈ°λ°μ λμ μ¬μμ± μκ³ λ¦¬μ¦μΈ Parametric dynamic movement primitives(PDMP)μ κΈ°μ΄ν μκ³ λ¦¬μ¦μ μ μνλ©°, μ΄λ₯Ό ν΅ν΄ λ€μν μ무λ₯Ό μννλ λͺ¨λ°μΌ μ‘°μκΈ°μ κΆ€μ μ μμ±νλ€. κΈ°μ‘΄μ λμ μ¬μμ± μκ³ λ¦¬μ¦κ³Ό λ¬λ¦¬, μ΄ μ°κ΅¬λ μ 곡λ μμ°μμ ννλ λμμ λ¨μν μ¬μμ±νλ κ²μ κ·ΈμΉμ§ μκ³ , μλ‘μ΄ νκ²½μ λ§κ² μΌλ°ν νλ κ³Όμ μ ν¬ν¨νλ€. μ΄ λ
Όλ¬Έμμ μ μνλ μΌλ°ν κ³Όμ μ PDMPsμ λ΄λΆ νλΌλ―Έν° κ°μΈ μ€νμΌ νλΌλ―Έν°μ νκ²½ λ³μ μ¬μ΄μ λΉμ ν κ΄κ³λ₯Ό κ°μ°μ€ νκ· κΈ°λ² (Gaussian process regression, GPR)μ μ΄μ©νμ¬ μμμ μΌλ‘ νννλ€. μ μλ κΈ°λ²μ λν μ΅μ μμ°λ₯Ό νμ΅νλ λ°©μμ ν΅ν΄ κ°λ ₯ν μ΅μ μ€μκ° κ²½λ‘ κ³ν κΈ°λ²μΌλ‘λ μμ©λ μ μλ€.
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Όλ¬Έμ 볡μ‘ν μ무μμ μ μ©λ μ μλ PDMPsμ νμ₯ κΈ°λ²μΈ seg-PDMPsλ₯Ό μ μνλ€. μ΄ μ κ·Όλ°©μμ 볡μ‘ν μλ¬΄κ° μΌλ°μ μΌλ‘ 볡μκ°μ κ°λ¨ν νμ μμ
μΌλ‘ ꡬμ±λλ€κ³ κ°μ νλ€. κΈ°μ‘΄ PDMPsμ λ¬λ¦¬ seg-PDMPsλ μ 체 κΆ€μ μ νμ μμ
μ λνλ΄λ μ¬λ¬ κ°μ λ¨μ λμμΌλ‘ λΆν νκ³ , κ° λ¨μλμμ λν΄ μ¬λ¬κ°μ PDMPsλ₯Ό ꡬμ±νλ€. κ° λ¨μ λμ λ³λ‘ μμ±λ PDMPsλ ν΅ν©λ νλ μμν¬λ΄μμ λ¨κ³ κ²°μ νλ‘μΈμ€λ₯Ό ν΅ν΄ μλμ μΌλ‘ νΈμΆλλ€. κ° λ¨κ³ λ³λ‘ λ¨μ λμμ μννκΈ° μν μκ° λ° νμ λͺ©νμ μ κ°μ°μ€ 곡μ νκ·(GPR)λ₯Ό μ΄μ©ν νκ²½λ³μμμμ κ΄κ³μμ ν΅ν΄ μ»λλ€. κ²°κ³Όμ μΌλ‘, μ΄ μ°κ΅¬λ μ 체μ μΌλ‘ μꡬλλ μμ°μ μλ₯Ό ν¨κ³Όμ μΌλ‘ μ€μΌ λΏ μλλΌ, κ° λ¨μλμμ νν μ±λ₯μ κ°μ νλ€.
μ μλ μκ³ λ¦¬μ¦μ νλ λͺ¨λ°μΌ λ‘λ΄ μ‘°μκΈ° μ€νμ ν΅νμ¬ κ²μ¦λλ€. μΈ κ°μ§μ μλ리μ€κ° λ³Έ λ
Όλ¬Έμμ λ€λ£¨μ΄μ§λ©°, ν곡 μ΄μ‘κ³Ό κ΄λ ¨λ 첫 λ κ°μ§ μλ리μ€λ PDMPs κΈ°λ²μ΄ λ‘λ΄ μ‘°μκΈ°μμ λΉ λ₯Έ μ μμ±, μ무 ν¨μ¨μ±κ³Ό μμ μ± λͺ¨λ λ§μ‘±νλ κ²μ μ
μ¦νλ€. λ§μ§λ§ μλ리μ€λ μ§μ μ°¨λμ μ΄μ©ν λ κ°μ λ‘λ΄ μ‘°μκΈ°μ λν μ€νμΌλ‘ 볡μ‘ν μ무 μνμ νκΈ° μν΄ νμ₯λ κΈ°λ²μΈ seg-PDMPsκ° ν¨κ³Όμ μΌλ‘ λ³ννλ νκ²½μμ μΌλ°νλ λμμ μμ±ν¨μ κ²μ¦νλ€.1 Introduction 1
1.1 Motivations 1
1.2 Literature Survey 3
1.2.1 Conventional Motion Planning in Mobile Manipulations 3
1.2.2 Motion Representation Algorithms 5
1.2.3 Safety-guaranteed Motion Representation Algorithms 7
1.3 Research Objectives and Contributions 7
1.3.1 Motion Generalization in Motion Representation Algorithm 9
1.3.2 Motion Generalization with Safety Guarantee 9
1.3.3 Motion Generalization for Complex Missions 10
1.4 Thesis Organization 11
2 Background 12
2.1 DMPs 12
2.2 Mobile Manipulation Systems 13
2.2.1 Single Mobile Manipulation 14
2.2.2 Cooperative Mobile Manipulations 14
2.3 Experimental Setup 17
2.3.1 Test-beds for Aerial Manipulators 17
2.3.2 Test-beds for Robot Manipulators with Ground Vehicles 17
3 Motion Generalization in Motion Representation Algorithm 22
3.1 Parametric Dynamic Movement Primitives 22
3.2 Generalization Process in PDMPs 26
3.2.1 Environmental Parameters 26
3.2.2 Mapping Function 26
3.3 Simulation Results 29
3.3.1 Two-dimensional Hurdling Motion 29
3.3.2 Cooperative Aerial Transportation 30
4 Motion Generalization with Safety Guarantee 36
4.1 Safety Criterion in Style Parameter 36
4.2 Demonstration Management 39
4.3 Simulation Validation 42
4.3.1 Two-dimensional Hurdling Motion 46
4.3.2 Cooperative Aerial Transportation 47
5 Motion Generalization for Complex Missions 51
5.1 Overall Structure of Seg-PDMPs 51
5.2 Motion Segments 53
5.3 Phase-decision Process 54
5.4 Seg-PDMPs for Single Phase 54
5.5 Simulation Results 55
5.5.1 Initial/terminal Offsets 56
5.5.2 Style Generalization 59
5.5.3 Recombination 61
6 Experimental Validation and Results 63
6.1 Cooperative Aerial Transportation 63
6.2 Cooperative Mobile Hang-dry Mission 70
6.2.1 Demonstrations 70
6.2.2 Simulation Validation 72
6.2.3 Experimental Results 78
7 Conclusions 82
Abstract (in Korean) 93Docto
Human-Robot Collaboration in Automotive Assembly
In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robotsβ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC
HERMIES-3: A step toward autonomous mobility, manipulation, and perception
HERMIES-III is an autonomous robot comprised of a seven degree-of-freedom (DOF) manipulator designed for human scale tasks, a laser range finder, a sonar array, an omni-directional wheel-driven chassis, multiple cameras, and a dual computer system containing a 16-node hypercube expandable to 128 nodes. The current experimental program involves performance of human-scale tasks (e.g., valve manipulation, use of tools), integration of a dexterous manipulator and platform motion in geometrically complex environments, and effective use of multiple cooperating robots (HERMIES-IIB and HERMIES-III). The environment in which the robots operate has been designed to include multiple valves, pipes, meters, obstacles on the floor, valves occluded from view, and multiple paths of differing navigation complexity. The ongoing research program supports the development of autonomous capability for HERMIES-IIB and III to perform complex navigation and manipulation under time constraints, while dealing with imprecise sensory information
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