Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertion

Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For instance, industrial assembly tasks frequently involve tight insertions where the clearance is less than $0.1$ mm and can even be negative when dealing with a deformable receptacle. This narrow clearance leads to complex contact dynamics that are difficult to model accurately in simulation, making it challenging to transfer simulation-learned policies to real-world robots. In this paper, we propose a novel framework for robustly learning manipulation skills for real-world tasks using only the simulated data. Our framework consists of two main components: the ``Force Planner'' and the ``Gain Tuner''. The Force Planner is responsible for planning both the robot motion and desired contact forces, while the Gain Tuner dynamically adjusts the compliance control gains to accurately track the desired contact forces during task execution. The key insight of this work is that by adaptively adjusting the robot's compliance control gains during task execution, we can modulate contact forces in the new environment, thereby generating trajectories similar to those trained in simulation and narrows the sim-to-real gap. Experimental results show that our method, trained in simulation on a generic square peg-and-hole task, can generalize to a variety of real-world insertion tasks involving narrow or even negative clearances, all without requiring any fine-tuning

Exploration of robotic-wheel technology for enhanced urban mobility and city scale omni-directional personal transportation

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (leaves 50-52).Mobility is traditionally thought of as freedom to access more goods and services. However, in my view, mobility is also largely about personal freedom, i.e., the ability to exceed one's physical limitations, in essence, to become "more than human" in physical capabilities. This thesis explores novel designs for omni-directional motion in a mobility scooter, car and bus with the aim of increasing personal mobility and freedom. What links these designs is the use of split active caster wheel robot technology. In the first section, societal and technological impacts of omni-directional motion in the city are examined. The second section of the thesis presents built and rendered prototypes of these three designs. The third and final section, evaluates implementation issues including robotic controls and an algorithm necessary for real world omni-directional mobility.by Raul-David Valdivia Poblano.S.M

Steering for a Class of Dynamic Nonholonomic Systems

In this paper we derive control algorithms for a class of dynamic nonholonomic steering problems, characterized as mechanical systems with nonholonomic constraints and symmetries. Recent research in geometric mechanics has led to a single, simplified framework that describes this class of systems, which includes examples such as wheeled mobile robots; undulatory robotic and biological locomotion systems, such as paramecia, inchworms, and snakes; and the reorientation of satellites and underwater vehicles. This geometric framework has also been applied to more unusual examples, such as the snakeboard robot, bicycles, the wobblestone, and the reorientation of a falling cat. We use this geometric framework as a basis for developing two types of control algorithms for such systems. The first is geared towards local controllability, using a perturbation approach to establish results similar to steering using sinusoids. The second method utilizes these results in applying more traditional steering algorithms for mobile robots, and is directed towards generating more non-local control methods of steering for this class of systems

Optimizing Dynamic Trajectories for Robustness to Disturbances Using Polytopic Projections

This paper focuses on robustness to disturbance forces and uncertain payloads. We present a novel formulation to optimize the robustness of dynamic trajectories. A straightforward transcription of this formulation into a nonlinear programming problem is not tractable for state-of-the-art solvers, but it is possible to overcome this complication by exploiting the structure induced by the kinematics of the robot. The non-trivial transcription proposed allows trajectory optimization frameworks to converge to highly robust dynamic solutions. We demonstrate the results of our approach using a quadruped robot equipped with a manipulator.Comment: Final accepted version to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020. Supplementary video: https://youtu.be/vDesP7IpTh

A contact-implicit direct trajectory optimization scheme for the study of legged maneuverability

For legged robots to move safely in unpredictable environments, they need to be manoeuvrable, but transient motions such as acceleration, deceleration and turning have been the subject of little research compared to constant-speed gait. They are difficult to study for two reasons: firstly, the way they are executed is highly sensitive to factors such as morphology and traction, and secondly, they can potentially be dangerous, especially when executed rapidly, or from high speeds. These challenges make it an ideal topic for study by simulation, as this allows all variables to be precisely controlled, and puts no human, animal or robotic subjects at risk. Trajectory optimization is a promising method for simulating these manoeuvres, because it allows complete motion trajectories to be generated when neither the input actuation nor the output motion is known. Furthermore, it produces solutions that optimize a given objective, such as minimizing the distance required to stop, or the effort exerted by the actuators throughout the motion. It has consequently become a popular technique for high-level motion planning in robotics, and for studying locomotion in biomechanics. In this dissertation, we present a novel approach to studying motion with trajectory optimization, by viewing it more as “trajectory generation” – a means of generating large quantities of synthetic data that can illuminate the differences between successful and unsuccessful motion strategies when studied in aggregate. One distinctive feature of this approach is the focus on whole-body models, which capture the specific morphology of the subject, rather than the highly-simplified “template” models that are typically used. Another is the use of “contact-implicit” methods, which allow an appropriate footfall sequence to be discovered, rather than requiring that it be defined upfront. Although contact-implicit methods are not novel, they are not widely-used, as they are computationally demanding, and unnecessary when studying comparatively-predictable constant speed locomotion. The second section of this dissertation describes innovations in the formulation of these trajectory optimization problems as nonlinear programming problems (NLPs). This “direct” approach allows these problems to be solved by general-purpose, open-source algorithms, making it accessible to scientists without the specialized applied mathematics knowledge required to solve NLPs. The design of the NLP has a significant impact on the accuracy of the result, the quality of the solution (with respect to the final value of the objective function), and the time required to solve the proble

심층 강화학습을 이용한 사람의 모션을 통한 이형적 캐릭터 제어기 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.사람의 모션을 이용한 로봇 컨트롤 인터페이스는 사용자의 직관과 로봇의 모터 능력을 합하여 위험한 환경에서 로봇의 유연한 작동을 만들어낸다. 하지만, 휴머노이드 외의 사족보행 로봇이나 육족보행 로봇을 위한 모션 인터페이스를 디자인 하는 것은 쉬운일이 아니다. 이것은 사람과 로봇 사이의 형태 차이로 오는 다이나믹스 차이와 제어 전략이 크게 차이나기 때문이다. 우리는 사람 사용자가 움직임을 통하여 사족보행 로봇에서 부드럽게 여러 과제를 수행할 수 있게끔 하는 새로운 모션 제어 시스템을 제안한다. 우리는 우선 캡쳐한 사람의 모션을 상응하는 로봇의 모션으로 리타겟 시킨다. 이때 상응하는 로봇의 모션은 유저가 의도한 의미를 내포하게 되며, 우리는 이를 지도학습 방법과 후처리 기술을 이용하여 가능케 하였다. 그 뒤 우리는 모션을 모사하는 학습을 커리큘럼 학습과 병행하여 주어진 리타겟된 참조 모션을 따라가는 제어 정책을 생성하였다. 우리는 "전문가 집단"을 학습함으로 모션 리타게팅 모듈과 모션 모사 모듈의 성능을 크게 증가시켰다. 결과에서 볼 수 있듯, 우리의 시스템을 이용하여 사용자가 사족보행 로봇의 서있기, 앉기, 기울이기, 팔 뻗기, 걷기, 돌기와 같은 다양한 모터 과제들을 시뮬레이션 환경과 현실에서 둘 다 수행할 수 있었다. 우리는 연구의 성능을 평가하기 위하여 다양한 분석을 하였으며, 특히 우리 시스템의 각각의 요소들의 중요성을 보여줄 수 있는 실험들을 진행하였다.A human motion-based interface fuses operator intuitions with the motor capabilities of robots, enabling adaptable robot operations in dangerous environments. However, the challenge of designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is emerged from the different morphology and dynamics of a human controller, leading to an ambiguity of control strategy. We propose a novel control framework that allows human operators to execute various motor skills on a quadrupedal robot by their motion. Our system first retargets the captured human motion into the corresponding robot motion with the operator's intended semantics. The supervised learning and post-processing techniques allow this retargeting skill which is ambiguity-free and suitable for control policy training. To enable a robot to track a given retargeted motion, we then obtain the control policy from reinforcement learning that imitates the given reference motion with designed curriculums. We additionally enhance the system's performance by introducing a set of experts. Finally, we randomize the domain parameters to adapt the physically simulated motor skills to real-world tasks. We demonstrate that a human operator can perform various motor tasks using our system including standing, tilting, manipulating, sitting, walking, and steering on both physically simulated and real quadruped robots. We also analyze the performance of each system component ablation study.1 Introduction 1 2 Related Work 5 2.1 Legged Robot Control 5 2.2 Motion Imitation 6 2.3 Motion-based Control 7 3 Overview 9 4 Motion Retargeting Module 11 4.1 Motion Retargeting Network 12 4.2 Post-processing for Consistency 14 4.3 A Set of Experts for Multi-task Support 15 5 Motion Imitation Module 17 5.1 Background: Reinforcement Learning 18 5.2 Formulation of Motion Imitation 18 5.3 Curriculum Learning over Tasks and Difficulties 21 5.4 Hierarchical Control with States 21 5.5 Domain Randomization 22 6 Results and Analysis 23 6.1 Experimental Setup 23 6.2 Motion Performance 24 6.3 Analysis 28 6.4 Comparison to Other Methods 31 7 Conclusion And Future Work 32 Bibliography 34 Abstract (In Korean) 44 감사의 글 45석