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Unlocking the potential of underactuated and mobile robotic systems: DFKI presents latest research results at ICRA 2024
RicMonk: short Teaser
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This paper presents the design, analysis, and performance evaluation of RicMonk, a novel three-link brachiation robot equipped with passive hook-shaped grippers. Brachiation, an agile and energy-efficient mode of locomotion observed in primates, has inspired the development of RicMonk to explore versatile locomotion and maneuvers on ladder-like structures. The robot's anatomical resemblance to gibbons and the integration of a tail mechanism for energy injection contribute to its unique capabilities. The paper discusses the use of the Direct Collocation methodology for optimizing trajectories for the robot's dynamic behaviors and stabilization of these trajectories using a Time-varying Linear Quadratic Regulator. With RicMonk we demonstrate bidirectional brachiation, and provide comparative analysis with its predecessor, AcroMonk - a two-link brachiation robot, to demonstrate that the presence of a passive tail helps improve energy efficiency. The system design, controllers, and software implementation are publicly available on GitHub.
At the DFKI Robotics Innovation Center, scientists are working on the challenges and opportunities of underactuated robotics. Their groundbreaking research lays the foundation for a new generation of dynamic, physically intelligent robots that can perform complex tasks and react flexibly to their environment. Among the research presented at ICRA is the paper "RicMonk: A Three-Link Brachiation Robot with Passive Grippers for Energy-Efficient Brachiation". It presents a new type of robot inspired by primate locomotion, capable of brachiation along solid objects and characterized by its energy efficiency. The paper "Robust Co-Design of Canonical Underactuated Systems for Increased Certifiable Stability" describes an innovative co-design algorithm that improves the behavior and robustness of underactuated systems by combining trajectory optimization, stabilization, and design optimization. The paper "Gaussian Mixture Likelihood-based Adaptive MPC for Interactive Mobile Manipulators" presents a new method for adaptive control of mobile robots that allows them to better respond to dynamic changes in the environment.
In addition to the scientific contributions, the DFKI Robotics Innovation Center is also involved in the organization of two workshops as part of the conference program. The workshops "Loco-Manipulation: Algorithms, Challenges & Applications" and "Co-Design in Robotics: Theory, Practice, and Challenges" invite participants to exchange ideas and collaborate with other experts in the robotics community.
The accepted papers in detail:
RicMonk: A Three-Link Brachiation Robot with Passive Grippers for Energy-Efficient Brachiation by Grama Srinivas Shourie Grama Srinivas Shourie, Mahdi Javadi, Shivesh Kumar, Hossein Zamani Boroujeni, Frank Kirchner
The paper presents RicMonk, a three-link robot with passive hook-shaped grippers. It is capable of brachiating by alternately gripping and releasing its limbs on fixed objects. This type of movement, which is observed in primates, allows for versatile movement on ladder-like structures. The robot is anatomically similar to a gibbon with a tail mechanism for energy supply. The paper describes the application of the direct collocation method, a numerical optimization technique used for trajectory optimization and robot stabilization. A Time-varying Linear Quadratic Regulator is employed to stabilize dynamic systems with time-varying behavior. As RicMonk demonstrates bidirectional brachiation, the paper also provides a comparative analysis with its predecessor, AcroMonk – a two-link brachiating robot – to show the improvement in energy efficiency facilitated by the presence of a passive tail. The system design, controllers, and software implementation are publicly available on GitHub.
Link: https://github.com/dfki-ric-underactuated-lab/ricmonk
Contact: shivesh.kumar[at]dfki.de
Robust Co-Design of Canonical Underactuated Systems for Increased Certifiable Stability by Federico Girlanda, Shivesh Kumar, Lasse Shala, Frank Kirchner
Optimal behavior of a system to perform a specific task can be achieved by exploiting the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, i.e., systems that have fewer actuators than degrees of freedom and thus require more sophisticated control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design Optimization (RTC-D). RTC-D consists of two layers: an inner optimization layer (RTC) and a design optimization layer. The RTC layer performs direct transcription (DIRTRAN) to identify a nominal trajectory while simultaneously determining optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). The RTC-D extends this functionality by maximizing system robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis that provides a formal stability guarantee for off-nominal states. To validate the proposed algorithm, extensive simulations are performed on two underactuated systems: the torque-limited simple pendulum and the cart pole. The results demonstrate improved robustness to off-nominal initial conditions, while real system experiments show increased resilience to torque perturbations.
Link: https://dfki-ric-underactuated-lab.github.io/robust_codesign/
Contact: shivesh.kumar[at]dfki.de
Gaussian Mixture Likelihood-based Adaptive MPC for Interactive Mobile Manipulators by Dimitrios Rakovitis, Dennis Mronga
Today, mobile robots are often used for real-world interaction tasks, such as opening doors or performing pick-and-place tasks. When used in real-world environments, the adaptation of robot controllers to uncertain contact dynamics is a significant challenge. Adaptive Model Predictive Control (AMPC) is an approach to control robot motions while adapting to uncertain or changing dynamics. However, most of the existing AMPC approaches used in mobile manipulation require either expert tuning or extensive training, making it very difficult to introduce novel or diverse tasks. In addition, the adaptation of multiple independent environmental parameters is usually not considered in the AMPC formulation. In this work, a hierarchical approach that uses Gaussian Mixture Models (GMMs) and Gaussian Mixture Regression (GMR) is presented to predict the dynamic model parameters of MPC based on proprioceptive measurements and to perform tasks with multiple unknown environmental parameters. The approach is evaluated in simulation and in real experiments on a mobile manipulator and compared to several baseline methods. It is shown to outperform standard MPC and an existing AMPC approach on several tasks such as pick-and-place, pushing, and door opening.
Link: https://rakovitisd.github.io/gmL_MPC.github.io/
Contact: dennis.mronga[at]dfki.de
More information about the workshops with DFKI participation:
Loco-Manipulation: Algorithms, Challenges & Applications by Oliver Urbann (Fraunhofer IML), Julian Eßer (Fraunhofer IML), Ioannis Havoutis (Oxford University), Shivesh Kumar (Chalmers/DFKI), Gabriel Margolis (MIT), Carlos Mastalli (Heriot-Watt University), Claudio Semini (IIT), Olivier Stasse (LAAS-CNRS).
Link: https://sites.google.com/view/loco-manipulation-icra24/home
Contact: shivesh.kumar[at]dfki.de
Co-design in Robotics: Theory, Practice, and Challenges by Cynthia Sung (GRASP Lab, UPenn), Darwin Lau (Chinese University of Hong Kong), Hannah Stuart (UC Berkeley), Pauline Pounds (University of Queensland), Shivesh Kumar (Chalmers/DFKI), Patrick M. Wensing (Uni Notre Dame)
Link: https://www.robotmechanisms.org/activities/icra-2024-codesign
Contact: shivesh.kumar[at]dfki.de