36 accepted papers
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 rec
The problem of generalization in learning from demonstration (LfD) has received considerable attention over the years, particularly within the context of movement primitives, where a number of approaches have emerged. Recently, two important approaches have gained recognition. While one leverages vi
Robot behavior designers commonly select one method – e.g. A* or RRT – that is assumed to have the appropriate trade-off for a given domain between computational load, computation time, and the quality of the result of the method. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xml
Explainability is vital for establishing user trust, also in robotics. Recently, foundation models (e.g. vision-language models, VLMs) fostered a wave of embodied agents that answer arbitrary queries about their environment and their interactions with it. However, naively prompting VLMs to answer qu
Guided Reinforcement Learning (RL) presents an effective approach for robots to acquire skills efficiently, directly in real-world environments. Recent works suggest that incorporating hard constraints into RL can expedite the learning of manipulation tasks, enhance safety, and reduce the complexity
Virtual Fixtures (VFs) provide haptic feedback for teleoperation, typically requiring distinct input modalities for different phases of a task. This often results in vision- and position-based fixtures. Vision-based fixtures, particularly, require the handling of visual uncertainty, as well as targe
Mobile manipulation tasks in unstructured environments remain challenging for autonomous robots. The need to employ a diverse set of software and hardware components to solve the various subtasks inevitably increases system complexity. Knowledge exchange among such diverse components renders them hi
Assistive robots promise to be of great help to wheelchair users with motor impairments, for example for activities of daily living. Using shared control to provide task-specific assistance – for instance with the Shared Control Templates (SCT) framework – facilitates user control, even with low-dim…
Shared control enables users with motor impairments to control high-dimensional assistive robots with low-dimensional user interfaces. The challenge is to simultaneously 1) provide support for completing daily living tasks 2) enable sufficient freedom of movement to foster user empowerment 3) ensure
Due to their flexibility and ease of use, Dynamical Movement Primitives (DMPs) are widely used in robotics applications and research. DMPs combine linear dynamical systems to achieve robustness to perturbations and adaptation to moving targets with non-linear function approximators to fit a wide ran…
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…
We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of…
Purposeful interaction with objects usually requires certain constraints to be respected, e.g. keeping a bottle upright to avoid spilling. In reinforcement learning, such constraints are typically encoded in the reward function. As a consequence, constraints can only be learned by violating them. Th…
Heterogeneous teams of multiple mobile robots will be important for future scientific explorations of extraterrestrial surfaces or hazardous areas. Mission operation in such harsh, unknown environments poses diverse challenges. Robots need to cooperate autonomously due to the large network latency t…
From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised…
Virtual Fixtures facilitate teleoperation, for in-stance by guiding the human operator. Developing these Virtual Fixtures in tasks with tight tolerances remains challenging. Fixtures with a high stiffness allow for more precise guidance, whereas a lower stiffness is required to allow for corrections…
In tendon-driven robotic hands, tendons are usually routed along several pulleys. The resulting friction is often substantial, and must therefore be modelled and estimated, for instance for accurate control and contact detection. Common approaches for friction estimation consider special dedicated s…
Controlling a robotic arm to achieve manipulation tasks is challenging for humans. Especially if only low-dimensional input signals can be provided, as is often the case for users with motor impairments. Using shared control to provide task-specific guidance and constraints facilitates control – for…
Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior…
Assistive robots aim to help humans with impairments execute motor tasks in everyday household environments. Controlling the end-effector of such robots directly, for instance with a joystick, is often cumbersome. Shared control methods, like Shared Control Templates (SCTs) [1], have therefore been
This work presents a novel object-level control framework for the dexterous in-hand manipulation of objects with torque-controlled robotic hands. The proposed impedance-based controller realizes the compliant 6-DOF positioning of a grasped object. Enabled by the in-hand localization, the slippage of
The autonomous assembly of customized products is highly demanded in future manufacturing scenarios. This requires robotic systems being able to adapt to individual products without increasing overall production time. However, increasingly complex assemblies lead to a growing number of potential ass
Nowadays, robots are mechanically able to perform highly demanding tasks, where AI-based planning methods are used to schedule a sequence of actions that result in the desired effect. However, it is not always possible to know the exact outcome of an action in advance, as failure situations may occu…
We present an approach for the autonomous, robust execution of peg-in-hole assembly tasks. We build on a sampling-based state estimation framework, in which samples are weighted according to their consistency with the position and joint torque measurements. The key idea is to reuse these samples in…
Light-weight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we…
Robot task programming often leads to inefficient plans, as opportunities for parallelization and precomputation are usually not exploited by the programmer. This inefficiency is often especially obvious in mobile manipulation, where path planning and pose estimation algorithms are time-consuming op
Intuitive robot programming for non-experts will be essential to increasing automation in small and medium-sized enterprises. Programming by demonstration (PbD) is a fast and intuitive approach, whereas programs created with task-level programming (TLP) are easy to understand and flexible in their e
Due to shorter product life cycles and increasing customization, production lines must be able to quickly adapt to novel product variants. This requires the automatic generation of assembly sequence plans from product specifications, as manual engineering of plans is slow and labor intensive. The ma
Sending humans to Mars' surface to build habitats is, for now, prohibitively dangerous and costly. An alternative is to have humans in orbiters, teleoperating robots on Mars to construct habitats, deferring human arrival until these habitats are finished. This paper describes the Kontur-2 experiment…
In the context of space missions and terrestrial applications, both mission goals and task implementations for autonomous robots are becoming increasingly complex. Thus, the challenge of monitoring the achievement of task objectives and checking the correctness of their implementation is becoming mo
For a robot to perform complex manipulation tasks, such as an in-hand manipulation, knowledge about the state of the grasp is required at all times. Moreover, even simple pick-and-place tasks may fail because unexpected motions of the object during the grasp are not accounted for. This letter propos
Current ergonomic assessment procedures require observation and manual annotation of postures by an expert, after which ergonomic scores are inferred from these annotations. Our aim is to automate this procedure and to enable robots to optimize their behavior with respect to such scores. A particula…
In co-manipulation, humans and robots solve manipulation tasks together. Virtual guides are important tools for co-manipulation, as they constrain the movement of the robot to avoid undesirable effects, such as collisions with the environment. Defining virtual guides is often a laborious task requir…
Members of a team are able to coordinate their actions by anticipating the intentions of others. Achieving such implicit coordination between humans and robots requires humans to be able to quickly and robustly predict the robot's intentions, i.e. the robot should demonstrate a behavior that is legi…