Frank Kirchner

State estimation is a key component in model-based control of walking robots and, more broadly, applicable wherever hidden variables must be inferred. The Kalman filter is widely used to estimate floating-base position and velocity by fusing multiple sensing modalities. However, tuning noise parameters is challenging and typically requires expert knowledge. Moreover, fixed noise parameters are unsuitable for varying gaits and environments. We propose an online adaptation strategy for the process noise covariance matrix Q and the measurement noise covariance matrix R. Specifically, we introduce a filter residual and innovation-based covariance adaptation method for legged robot state estimation and evaluate it against a baseline approach relying on IMU and foot force measurements. The proposed adaptation is implemented within an Invariant Extended Kalman Filter (InEKF) fusing IMU and leg kinematics. Experiments on indoor and outdoor datasets with a Unitree Go2 quadruped show that adapting R is sufficient and improves accuracy by 25% for the trotting gait compared to the fixed-tuned InEKF. Finally, the proposed residual-based adaptation achieves comparable performance to the foot force approach, without requiring foot force measurements or additional parameter tuning.

Dynamical Movement Primitives (DMPs) provide a stable and generalizable framework for learning from demonstration, but their classical formulation is limited to pose trajectories with predefined temporal dynamics, reducing their applicability to contact-rich manipulation tasks. We present 3C- DMP (Composite, Contextual, and Compliant Dynamical Movement Primitives), an extension that enables multi-modal and adaptive skill reproduction. The framework introduces: (i) a composite representation that synchronizes Cartesian pose, wrench trajectories, and gripper actions through a shared canonical system over segmented trajectories; (ii) a compliant wrench encoding that directly represents forces and torques in the forcing-term space, ensuring spatio-temporal coupling with motion; and (iii) a contextual phase adaptation mechanism in which a neural modulation network adjusts execution speed online based on task context and tracking error. The framework is implemented in ROS 2 and evaluated on a KUKA LBR iiwa across four tasks: electrical plug insertion, gear assembly, Push-T, and human-to-robot object handover. Using a single demonstration per task, 3C-DMP improves pose–wrench and gripper synchronization over standard DMPs in direct baseline comparisons, generalizes to changed Push-T boundary conditions with success rates of up to 80%, and supports contact-triggered handover timing. These results demonstrate the potential of 3C-DMP as a sample-efficient foundation for compliant, context-aware robot skill learning.

Celestial bodies in the solar system have long been of particular interest in space science. Some questions, e.g., those concerning the origin of life, require on-site landing and exploration. Due to signal delay, some degree of autonomy provided by artificial intelligence (AI) is needed. Motivated by planetary exploration missions, the German Research Center for Artificial Intelligence (DFKI) has developed methods for (semi-)autonomous control of vehicles and robots on extraterrestrial bodies. To validate the software, we conduct extensive field tests in terrestrial analog environments. Field tests can be seen as an intermediate step between development and laboratory testing and real-world deployment in an extraterrestrial environment. This paper describes the challenges of testing AI and robotic systems in analog environments, with a focus on the additional dependencies that arise during the preparation and execution of such field tests. The robots and software tested in these field tests are based on more than a decade of development across various projects, covering a wide range of AI systems and applications, including geometric planning, probabilistic perception, deep learning, and robot construction for open challenges in planetary exploration.

The term hydrobatics is inspired by aerobatics and refers to a new class of highly dynamic maneuvers performed by Autonomous Underwater Vehicles (AUVs) with maximum agility and precision. Due to the complex interaction with the ocean environment, advanced control systems are required, which allow for precise maneuvering despite model uncertainties and external disturbances such as ocean currents. In addition, time-critical maneuvers often demand formal timing guarantees, such as fixed-time or finite-time convergence. Nonlinear Model Predictive Control (NMPC) is one of the dominant approaches for tracking control, due to its ability to handle nonlinear models and its constraint satisfaction guarantees. However, classical control strategies - including Sliding Mode Control (SMC), Backstepping, and Incremental Nonlinear Dynamic Inversion (INDI) also remain relevant, due to their robustness to model uncertainties and lower computational demands. This work provides an overview of the recent advances in control strategies for hydrobatic AUVs, with a focus on robustness, performance guarantees, and timing-aware control design.

In robotics, many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields to achieve reliable control in diverse tasks. To get a clear understanding of their individual strengths and weaknesses and their applicability in real-world robotic scenarios, it is important to benchmark and compare their performances not only in a simulation but also on real hardware. The second AI Olympics with RealAIGym competition was held at the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) to contribute to this cause and evaluate different controllers according to their ability to solve a dynamic control problem on an underactuated double-pendulum system (Figure 1) with chaotic dynamics. This article describes the four different RL methods submitted by the participating teams; presents their performance in the swing-up task on a real double pendulum, measured against various criteria; and discusses their transferability from simulation to real (sim-to-real) hardware and their robustness to external disturbances.

Planetary exploration missions require robots capable of navigating extreme and unknown environments. While wheeled rovers have dominated past missions, their mobility is limited to traversable surfaces. Legged robots, especially quadrupeds, can overcome these limitations by handling uneven, obstacle-rich, and deformable terrains. However, deploying such robots in unknown conditions is challenging due to the need for environment-specific control, which is infeasible when terrain and robot parameters are uncertain. This work presents a modular control framework that combines model-based dynamic control with online model adaptation and adaptive footstep planning to address uncertainties in both robot and terrain properties. The framework includes state estimation for quadrupeds with and without contact sensing, supports runtime reconfiguration, and is integrated into ROS 2 with open-source availability. Its performance was validated on two quadruped platforms, multiple hardware architectures, and in a volcano field test, where the robot walked over 700 m.