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1. Contact-Aware Controller Design for Complementarity Systems

   https://doi.org/10.1109/ICRA40945.2020.9197568  

   Aydinoglu, Alp; Preciado, Victor M.; Posa, Michael (May 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA))

   While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, by exploiting the complementarity structure of contact dynamics, we propose a control framework which can close the loop on rich, tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on three different underactuated, multi-contact robotics problems. 

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2. Predicting Motion Plans for Articulating Everyday Objects

   Gupta, Arjun; Shepherd, Max; Gupta, Saurabh (May 2023, International Conference on Robotics and Automation (ICRA))

   Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that simulates articulated objects placed in real scenes. We then introduce SeqIK+θ0, a fast and flexible representation for motion plans. Finally, we learn models that use SeqIK+θ0 to quickly predict motion plans for articulating novel objects at test time. Experimental evaluation shows improved speed and accuracy at generating motion plans than pure search-based methods and pure learning methods. 

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3. Learning to stabilize high-dimensional unknown systems using Lyapunov-guided exploration

   Zhang, Songyuan; Fan, Chuchu (July 2024, Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR)

   Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that can hardly be accurately modeled with differential equations. The Lyapunov theory offers a solution for stabilizing control systems, still, current methods relying on Lyapunov functions require access to complete dynamics or samples of system executions throughout the entire state space. Consequently, they are impractical for high-dimensional systems. This paper introduces a novel framework, LYapunov-Guided Exploration (LYGE), for learning stabilizing controllers tailored to high-dimensional, unknown systems. LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet model featuring a 16D state space and a 4D input space. Experiments indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE reduces the distance to the goal by 50% while requiring only 5% to 32% of the samples. Furthermore, we demonstrate that our algorithm can be extended to learn controllers guided by other certificate functions for unknown systems. 

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4. Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold

   https://doi.org/10.1109/ICRA40945.2020.9197533  

   Jorgensen, Steven Jens; Vedantam, Mihir; Gupta, Ryan; Cappel, Henry; Sentis, Luis (May 2020, International Conference on Robotics and Automation)

   We present a method that finds locomanipulation plans that perform simultaneous locomotion and manipulation of objects for a desired end-effector trajectory. Key to our approach is to consider an injective locomotion constraint manifold that defines the locomotion scheme of the robot and then using this constraint manifold to search for admissible manipulation trajectories. The problem is formulated as a weighted-A* graph search whose planner output is a sequence of contact transitions and a path progression trajectory to construct the whole-body kinodynamic locomanipulation plan. We also provide a method for computing, visualizing, and learning the locomanipulability region, which is used to efficiently evaluate the edge transition feasibility during the graph search. Numerical simulations are performed with the NASA Valkyrie robot platform that utilizes a dynamic locomotion approach, called the divergent-component-of-motion (DCM), on two example locomanipulation scenarios. 

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5. Momentum based Whole-Body Optimal Planning for a Single-Spherical-Wheeled Balancing Mobile Manipulator

   Shu, R.; Hollis, R. (October 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   In this paper, we present a planning and control framework for dynamic, whole-body motions for dynamically stable shape-accelerating mobile manipulators. This class of robots are inherently unstable and require careful coordination between the upper and lower body to maintain balance while performing manipulation tasks. Solutions to this problem either use a complex, full-body nonlinear dynamic model of the robot or a highly simplified model of the robot. Here we explore the use of centroidal dynamics which has recently become a popular approach for designing balancing controllers for humanoid robots. We describe a framework where we first solve a trajectory optimization problem offline. We define balancing for a ballbot in terms of the centroidal momentum instead of other approaches like ZMP or angular velocity that are more commonly used. The generated motion is tracked using a PD- PID cascading balancing controller for the body and torque controller for the arms. We demonstrate that this framework is capable of generating dynamic motion plans and control inputs with examples on the CMU ballbot, a single-spherical-wheeled balancing mobile manipulator. 

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