Learning-based methods excel at robot motion generation but remain limited in contact-rich physical interaction. Impedance control provides stable and safe contact behavior but requires task-specific tuning of stiffness and damping parameters. We present Diffusion-Based Impedance Learning, a framework that bridges these paradigms by combining generative modeling with energy-consistent impedance control. A Transformer-based Diffusion Model, conditioned via cross-attention on measured external wrenches, reconstructs simulated Zero-Force Trajectories (sZFTs) that represent contact-consistent equilibrium behavior. A SLERP-based quaternion noise scheduler preserves geometric consistency for rotations on the unit sphere. The reconstructed sZFT is used by an energy-based estimator to adapt impedance online through directional stiffness and damping modulation. Trained on parkour and robot-assisted therapy demonstrations collected via Apple Vision Pro teleoperation, the model achieves sub-millimeter positional and sub-degree rotational accuracy using only tens of thousands of samples. Deployed in real-time torque control on a KUKA LBR iiwa, the approach enables smooth obstacle traversal and generalizes to unseen tasks, achieving 100% success in multi-geometry peg-in-hole insertion. The code for all experiments is publicly available on GitHub and videos of the experiments are available on the project website.

An emerging trend in the field of human-robot collaboration is the disassembly of end-of-life products. Safety is a crucial requirement of the disassembly process since worn-out or damaged products could break, possibly resulting in dangerous behavior of the robot. To protect the user from such behavior, this work addresses this challenge through the implementation of an energy-aware Cartesian impedance controller, combined with virtual workspace restrictions. Hereby, the passivity of the robotic system is ensured. The paper proposed two approaches to ensure the passivity of the system when subjected to workspace restrictions due to unplanned interactions and contact loss. The first approach employs an augmented energy tank with restricted energy flow. The second approach monitors the overall energy flow, regulating and separating non-passive behavior, caused by workspace restrictions. The approaches are evaluated and compared with each other, by using a KUKA LBR iiwa robot. The results highlight the potential of virtual workspace restrictions in human-robot collaborative disassembly tasks.

In this paper, a kinematically modular approach to robot control is presented. The method involves structures called Elementary Dynamic Actions and a network model combining these elements. With this control framework, a rich repertoire of movements can be generated by combination of basic modules. The problems of solving inverse kinematics, managing kinematic singularity and kinematic redundancy are avoided. The modular approach is robust against contact and physical interaction, which makes it particularly effective for contact-rich manipulation. Each kinematic module can be learned by Imitation Learning, thereby resulting in a modular learning strategy for robot control. The theoretical foundations and their implementation on a real robot are presented. Using a KUKA LBR iiwa robot, three tasks were considered: (1) generating a sequence of discrete movements, (2) generating a combination of discrete and rhythmic movements, and (3) a drawing and erasing task. The results obtained show that this modular approach has the potential to simplify the generation of a diverse range of robot actions.

The control of kinematically redundant robots is often approached using nullspace projection, which requires precise models and can be computationally challenging. Humans have many more degrees of freedom than are required to accomplish their tasks, but given neuromechanical limitations, it seems unlikely that biology relies on precise models or complex computation. An alternative biologically inspired approach leverages the compositionality of mechanical impedance. In theory, nullspace projection eliminates any conflict between two tasks. In contrast, superposition of task-space impedance and a full-rank joint-space impedance may impose a task conflict. This work compared nullspace projection with impedance superposition during unconstrained motion and forceful physical interaction. In practice, despite their theoretical differences, we did not observe a substantial influence of the nullspace projector weighting matrix. We found that nullspace projection and impedance superposition both resulted in measurable task conflict. Remarkably, when the dimensionality of the nullspace was increased, impedance superposition was comparable to nullspace projection.

Humans are capable of managing multiple tasks simultaneously. It is widely assumed that human motor control can be emulated by impedance control. To achieve human-like behavior, however, the impedance parameters of multiple tasks may vary during task execution. We propose an algorithm that shapes task impedance as a function of the robot’s time-varying inertial properties. These properties involve virtually constrained masses and virtually constrained inertias that counteract a task in order to comply with a given constraint. In this work, we not only detect task conflicts, but also show how to handle them. Our method is able to control kinematically redundant robots. We developed a damping-design method that does not interfere with our desired Cartesian task-space behavior. The control approach was verified in experiments on a real robot. We compared our impedance shaping method with two alternative control approaches: simple impedance superposition and nullspace projection. Our method preserved the passivity while improving the Cartesian task performance of an impedance controller. The method has computational advantages, beneficial to control robots with many degrees of freedom.

To this day, most robots are installed behind safety fences, separated from the human. New use-case scenarios demand for collaborative robots, e.g. to assist the human with physically challenging tasks. These robots are mainly installed in work-environments with limited space, e.g. existing production lines. This brings certain challenges for the control of such robots. The presented work addresses a few of these challenges, namely: stable and safe behaviour in contact scenarios; avoidance of restricted workspace areas; prevention of joint limits in automatic mode and manual guidance. The control approach in this paper extents an Energy-aware Impedance controller by repulsive potential fields in order to comply with Cartesian and joint constraints. The presented controller was verified for a KUKA LBR iiwa 7 R800 in simulation as well as on the real robot.

Modern robots act in dynamic and partially unknown environments where path replanning can be mandatory if changes in the environment are observed. Task-prioritized control strategies are well known and effective solutions to ensure local adaptation of robot behaviour. The highest priority in a stack of tasks is typically given to the management of correct robot operation or safe interaction with the environment such as obstacles or joint limits avoidance, that we can consider as constraints. If a constraint makes impossible achieving a certain task, such as tracking a Cartesian trajectory, a local control algorithm partially sacrifices the latter which is only accomplished to the best of the robot's ability to generate internal motions. In this control framework, problems may occur in some applications, like in the surgical domain, where it is not safe that some tasks are simply sacrificed without prior notice. The contribution of this work is to introduce a coordinate invariant index, that is used to provide a geometrical interpretation of task conflicts in a task-priority control framework and to develop a method for on-line detection of algorithmic singularities, with the goal of increasing safety and performances during robot operations.