모션 프리머티브를 이용한 복잡한 로봇 임무 학습 및 일반화 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 항공우주공학과, 2020. 8. 김현진.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.시연 학습 기법(Learning from demonstrations, LfD)은 로봇이 특정 동작을 수행할 수 있도록 하는 유망한 동작 생성 기법이다. 로봇 조작기가 인간 사회에서 다양한 업무를 대체해 감에 따라, 다양한 임무를 수행하는 로봇의 동작을 생성하기 위해 LfD 알고리즘들은 널리 연구되고, 사용되고 있다. 본 논문은 LfD 기법 중 모션 프리머티브 기반의 동작 재생성 알고리즘인 Parametric dynamic movement primitives(PDMP)에 기초한 알고리즘을 제시하며, 이를 통해 다양한 임무를 수행하는 모바일 조작기의 궤적을 생성한다. 기존의 동작 재생성 알고리즘과 달리, 이 연구는 제공된 시연에서 표현된 동작을 단순히 재생성하는 것에 그치지 않고, 새로운 환경에 맞게 일반화 하는 과정을 포함한다. 이 논문에서 제시하는 일반화 과정은 PDMPs의 내부 파라미터 값인 스타일 파라미터와 환경 변수 사이의 비선형 관계를 가우스 회귀 기법 (Gaussian process regression, GPR)을 이용하여 수식적으로 표현한다. 제안된 기법은 또한 최적 시연를 학습하는 방식을 통해 강력한 최적 실시간 경로 계획 기법으로도 응용될 수 있다. 본 논문에서는 또한 로봇의 구동 안전성도 고려한다. 기존 연구들에서 다루어진 시연 관리 기술이 로봇의 구동 효율성을 개선하는 방향으로 제시된 것과 달리, 이 연구는 강한 구속조건으로 로봇의 구동 안전성을 확보하는 시연 관리 기술을 통해 안정성을 고려하는 새로운 방식을 제시한다. 제안된 방식은 스타일 파라미터 값 상에서 안전성 기준을 계산하며, 이 안전 기준을 통해 시연을 제거하는 일련의 작업을 수행한다. 또한, 제거된 시위를 안전 기준에 따라 입증된 시위로 대체하여 일반화 성능을 저하시키지 않도록 시위를 관리한다. 이를 통해 다수의 시연 각각 개별 동작 안전성 뿐 아니라 온라인 동작의 안전성까지 고려할 수 있으며, 실시간 로봇 조작기 운용시 안전성이 확보될 수 있다. 제안된 안정성을 고려한 시연 관리 기술은 또한 환경의 정적 설정이 변경되어 모든 시연을 교체해야 할 수 있는 상황에서 사용할 수 있는 시연들을 판별하고, 효율적으로 재사용하는 데 응용할 수 있다. 또한 본 논문은 복잡한 임무에서 적용될 수 있는 PDMPs의 확장 기법인 seg-PDMPs를 제시한다. 이 접근방식은 복잡한 임무가 일반적으로 복수개의 간단한 하위 작업으로 구성된다고 가정한다. 기존 PDMPs와 달리 seg-PDMPs는 전체 궤적을 하위 작업을 나타내는 여러 개의 단위 동작으로 분할하고, 각 단위동작에 대해 여러개의 PDMPs를 구성한다. 각 단위 동작 별로 생성된 PDMPs는 통합된 프레임워크내에서 단계 결정 프로세스를 통해 자동적으로 호출된다. 각 단계 별로 단위 동작을 수행하기 위한 시간 및 하위 목표점은 가우스 공정 회귀(GPR)를 이용한 환경변수와의의 관계식을 통해 얻는다. 결과적으로, 이 연구는 전체적으로 요구되는 시연의 수를 효과적으로 줄일 뿐 아니라, 각 단위동작의 표현 성능을 개선한다. 제안된 알고리즘은 협동 모바일 로봇 조작기 실험을 통하여 검증된다. 세 가지의 시나리오가 본 논문에서 다루어지며, 항공 운송과 관련된 첫 두 가지 시나리오는 PDMPs 기법이 로봇 조작기에서 빠른 적응성, 임무 효율성과 안전성 모두 만족하는 것을 입증한다. 마지막 시나리오는 지상 차량을 이용한 두 개의 로봇 조작기에 대한 실험으로 복잡한 임무 수행을 하기 위해 확장된 기법인 seg-PDMPs가 효과적으로 변화하는 환경에서 일반화된 동작을 생성함을 검증한다.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

Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations

The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts the performance measure and the feasibility of the predicted policy with BTMG parameters and task variations as inputs. Using the outputs of the model, we then construct a surrogate reward function that is utilized within an optimizer to maximize the performance of a task over BTMG parameters for a fixed task variation. To demonstrate the effectiveness of our proposed approach, we conducted experimental evaluations on push and obstacle avoidance tasks in simulation and with a real KUKA iiwa robot. Furthermore, we compared the performance of our approach with four baseline methods

Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies

Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework that incorporates Bayesian variational inference for learning flexible non-parametric multi-action policies, while simultaneously robustifying the policies against sources of error, by introducing and optimizing disturbances to create a richer demonstration dataset. This combinatorial approach forces the policy to adapt to challenging situations, enabling stable multi-action policies to be learned efficiently. The effectiveness of our proposed method is evaluated through simulations and real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm. Results show that, through improved flexibility and robustness, the learning performance and control safety are better than comparison methods.Comment: 7 pages, Accepted by the 2021 International Conference on Robotics and Automation (ICRA 2021

Learning Skill-based Industrial Robot Tasks with User Priors

Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot system to learn directly on the task. For a learning problem, a robot operator can typically specify the type and range of values of the parameters. Nevertheless, given their prior experience, robot operators should be able to help the learning process further by providing educated guesses about where in the parameter space potential optimal solutions could be found. Interestingly, such prior knowledge is not exploited in current robot learning frameworks. We introduce an approach that combines user priors and Bayesian optimization to allow fast optimization of robot industrial tasks at robot deployment time. We evaluate our method on three tasks that are learned in simulation as well as on two tasks that are learned directly on a real robot system. Additionally, we transfer knowledge from the corresponding simulation tasks by automatically constructing priors from well-performing configurations for learning on the real system. To handle potentially contradicting task objectives, the tasks are modeled as multi-objective problems. Our results show that operator priors, both user-specified and transferred, vastly accelerate the discovery of rich Pareto fronts, and typically produce final performance far superior to proposed baselines.Comment: 8 pages, 6 figures, accepted at 2022 IEEE International Conference on Automation Science and Engineering (CASE

A structured prediction approach for robot imitation learning

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework

A Structured Prediction Approach for Robot Imitation Learning

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework