Alin Albu-Schaffer

The three-dimensional divergent component of motion (3D-DCM) framework has been successfully utilized to generate center of mass (CoM) trajectories for various locomotion types. While the 3D-DCM encodes the CoM dynamics, it relies on the contact forces between the robot’s end effectors and the environment to realize the intended motion. In the original formulation of the 3D-DCM, the feasibility of contact forces concerning contact constraints is assumed, but a comprehensive analysis of this assumption is lacking. In this work, we address this gap by extending the 3D-DCM framework to incorporate contact constraints and dynamic stability of the system. This is achieved by encoding feasible CoM forces as geometric sets. We derive an analytical relationship that characterizes how these sets can be modulated by humanoid push recovery strategies. Building on these insights, we propose a push recovery algorithm that integrates ankle, hip, height-variation, and stepping strategies. The proposed method is evaluated through extensive experiments with the humanoid robot TORO, including scenarios of force-disturbed balancing, walking, and multi-contact configurations.

The accuracy of robotic forward kinematics is commonly improved by calibration. However, most calibration methods only take deterministic errors, such as inaccurate geometry and unknown stiffnesses, into account and neglect errors with stochastic characteristics, including joint friction, gear backlash and component wear. In order to incorporate these effects, this paper presents a calibration procedure for a probabilistic forward kinematics model which identifies both systematic errors and error sources whose combined effects are modeled as distributions within a kinematic chain. Additionally, we present a method for using the identified distributions to enhance task-relevant accuracy by minimizing the end-effector uncertainty resulting from such distributions in a task-specific way. Finally, this idea is demonstrated in an experiment with a research rover tasked with picking up a payload box from a lander. Here, we show that end-effector error in task-relevant directions can be reduced by 40% by choosing low-uncertainty over high-uncertainty configurations.

Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that enables open-vocabulary skill adaptation through a tool-based architecture maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre-trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability.

In humanoid motion planning, centroidal angular momentum (CAM) is often neglected or simplified due to the complexity arising from its nonlinear and non-holonomic nature. As a result, unmodeled CAM effects can compromise center of pressure (CoP) tracking and, consequently, reduce robustness during motion execution. To address this, we propose an online iterative algorithm that computes the induced CAM and updates the corresponding center of mass (CoM) trajectory accordingly. Since leg motion is the primary source of angular momentum in legged locomotion, the proposed framework also optimizes the swing-leg trajectories to generate the desired CAM while respecting kinematic hardware limits. This results in CAM-consistent whole-body motions that enable stable yet faster locomotion with reduced demand for maximum joint velocities. We demonstrate the effectiveness of our approach through experiments and simulations with the humanoid robot TORO in several locomotion scenarios, including balancing, walking, and running.

There exist numerous ways to achieve multi-tasking control in kinematically redundant robots to accomplish several goals simultaneously. In all approaches, regardless of the specific type of controller, one has to make a choice about the closed-loop inertia and consequently the dynamic task couplings. Here, we introduce a new control strategy which combines two fundamentally different properties that have not been brought together yet. First, we fully dynamically decouple all individual subtasks, which cannot be achieved with classical passivity-based or hierarchical approaches. Second, we provide high robustness in practice which is structurally not possible with any inverse-dynamics approaches enforcing a decoupled but constant closed-loop inertia. Beside formal proofs of stability and passivity, we compare our approach with the other categories in various simulations and experiments. Since the proposed controller is grounded on the fundamental property of full natural task-space decoupling, this underlying strategy and its benefits can also be transferred to other design methods such as quadratic programming, MPC, or learning-based approaches.

Surface finishing is a time-consuming, dangerous task, difficult to automate despite its necessity in many manufacturing processes. Its automation, particularly through robotics, increases productivity and relieves workers from health-critical tasks. However, challenges remain, as automated offline planning tools can result in certain areas being either neglected or overly processed. Ergodic control offers the possibility to cover target probability distributions in an online manner, by taking into account the observed coverage history. However, existing ergodic control approaches provide little flexibility in designing and adapting coverage strategies. Moreover, they come with simplifying assumptions, such as point-based dynamics, which are no longer valid for tasks where the robot is in contact with strongly varying curvatures on non-trivial surface geometries. In this work, we introduce a closed-form ergodic control framework that includes the tool imprint in the system modeling while simultaneously permitting the intuitive transfer of finish strategies, namely preferred motion directions. We build on the Spectral Multiscale Coverage (SMC) approach, augmenting it with a tool imprint model, as well as both target distributions and state-dependent movement directions extracted from human demonstrations. Through evaluations in a surface finishing task using a torque-controlled, 7-DoF, robot arm we show that our approach optimally covers surfaces according to the tool contact area, with robust error convergence.

Legged robots offer several advantages when navigating unstructured environments, but they often fall short of the efficiency achieved by wheeled robots. One promising strategy to improve their energy economy is to leverage their natural (unactuated) dynamics using elastic elements. This work explores that concept by designing energy-optimal control inputs through a unified, multi-stage framework. It starts with a novel energy injection technique to identify passive motion patterns by harnessing the system’s natural dynamics. This enables the discovery of passive solutions even in systems with energy dissipation caused by factors such as friction or plastic collisions. Building on these passive solutions, we then employ a continuation approach to derive energy-optimal control inputs for the fully actuated, dissipative robotic system. The method is tested on simulated models to demonstrate its applicability in both single- and multi-legged robotic systems. This analysis provides valuable insights into the design and operation of elastic legged robots, offering pathways to improve their efficiency and adaptability by exploiting the natural system dynamics.