41 accepted papers
Diffusion policies (DPs) achieve state-of-the-art performance on complex manipulation tasks by learning from large-scale demonstration datasets, often spanning multiple embodiments and environments. However, they cannot guarantee safe behavior, requiring external safety mechanisms. These, however, a…
In reinforcement learning (RL), it is often advantageous to consider additional constraints on the action space to ensure safety or action relevance. Existing work on such action-constrained RL faces challenges regarding effective policy updates, computational efficiency, and predictable runtime. R
Neural networks are ubiquitous. However, they are often sensitive to small input changes. Hence, to prevent unexpected behavior in safety-critical applications, their formal verification -- a notoriously hard problem -- is necessary. Many state-of-the-art verification algorithms use reachability ana…
Despite significant progress in post-hoc explanation methods for neural networks, many remain heuristic and lack provable guarantees. A key approach for obtaining explanations with provable guarantees is by identifying a cardinally-minimal subset of input features which by itself is provably suffici…
High-stakes, infrequent consumer decisions, such as housing selection, challenge conventional recommender systems due to sparse interaction signals, heterogeneous multi-criteria objectives, and high-dimensional feature spaces. This work presents an interactive preference elicitation framework that…
Conformal prediction is a popular uncertainty quantification method that augments a base predictor to return sets of predictions with statistically valid coverage guarantees. However, current methods are often computationally expensive and data-intensive, as they require constructing an uncertainty …
Calculating the inverse kinematics (IK) is a fundamental challenge in robotics. Compared to numerical or learning-based approaches, analytical IK provides higher efficiency and accuracy. However, existing analytical approaches are difficult to use in most applications, as they require human ingenuit
Efficient and safe integration of robots into human workspaces remains a significant challenge. The ISO 10218-2 standard defines permissible force thresholds that a robot is allowed to exert on humans, along with a simple model to estimate the impact force based on the impact velocity, the involved
Despite significant advancements in post-hoc explainability techniques for neural networks, many current methods rely on heuristics and do not provide formally provable guarantees over the explanations provided. Recent work has shown that it is possible to obtain explanations with formal guarantee…
Formulating the intended behavior of a dynamic system can be challenging. Signal temporal logic (STL) is frequently used for this purpose due to its suitability in formalizing comprehensible, modular, and versatile spatiotemporal specifications. Due to scaling issues with respect to the complexity o
Selecting an optimal robot, its base pose, and trajectory for a given task is currently mainly done by human expertise or trial and error. To evaluate automatic approaches to this combined optimization problem, we introduce a benchmark suite encompassing a unified format for robots, environments, an…
Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-intensive. To tackle the aforementioned issues, we pre
Industrial robots are essential for modern production but often struggle to adapt to new tasks. Modular (reconfigurable) robots can overcome this challenge by eliminating the need to replace the whole robot. However, finding the optimal assembly for a task remains difficult because a valid path has…
Formal verification of robotic tasks requires a simple yet conformant model of the used robot. We present the first work on generating reachset conformant models for robotic contact tasks considering hybrid (mixed continuous and discrete) dynamics. Reachset conformance requires that the set of reach…
Continuous action spaces in reinforcement learning (RL) are commonly defined as multidimensional intervals. While intervals usually reflect the action boundaries for tasks well, they can be challenging for learning because the typically large global action space leads to frequent exploration of irre…
Formal verification of neural networks is a challenging problem due to the complexity and nonlinearity of neural networks. It has been shown that polynomial zonotopes can tightly enclose the output set of a neural network. Unfortunately, the tight enclosure comes with additional complexity i…
Deep reinforcement learning (RL) has shown promising results in robot motion planning with first attempts in human-robot collaboration (HRC). However, a fair comparison of RL approaches in HRC under the constraint of guaranteed safety is yet to be made. We, therefore, present human-robot gym, a benc…
Industrial robots are designed as general-purpose hardware with limited ability to adapt to changing task requirements or environments. Modular robots, on the other hand, offer flexibility and can be easily customized to suit diverse needs. The morphology, i.e., the form and structure of a robot, si…
Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approaches typically treat user preferences as a reward, which requires a manual balance between task success and user satisfacti…
Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its…
The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. The calculation of robustness is based on evaluating the robustness of underlying predicates. However, the robustness of p
Deep Reinforcement Learning (RL) has shown promise in addressing complex robotic challenges. In real-world applications, RL is often accompanied by failsafe controllers as a last resort to avoid catastrophic events. While necessary for safety, these interventions can result in undesirable behaviors,…
Modular Reconfigurable Robots (MRRs) represent an exciting path forward for industrial robotics, opening up new possibilities for robot design. Compared to monolithic manipulators, they promise greater flexibility, improved maintainability, and cost-efficiency. However, there is no tool or standardi…
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipul…
Current safety mechanisms implementing industry standards for human-robot coexistence separate humans and robots through caging. Other approaches allowing humans to enter the workspace of manipulators do not provide formal safety guarantees. Thus, this study aims to facilitate the widespread adoptio…
The validation of online perception algorithms in automotive systems requires a large amount of ground-truth data. Since manual labeling is inefficient and error-prone, an automatic generation of accurate and reliable reference data is desirable. We present a post-processing approach based on a part
Humans must remain unharmed during their interaction with robots. We present a new method guaranteeing impact force limits when humans and robots share a workspace. Formal guarantees are realized using an online verification method, which plans and verifies fail-safe maneuvers through predicting rea…
Humans and robots are increasingly sharing their workspaces to benefit from the precision, endurance, and strength of machines and the universal capabilities of humans. Instead of performing time-consuming real experiments, computer simulations of humans could help to optimally orchestrate human and…
Flexible manufacturing and automation require robots that can be adapted to changing tasks. We propose to use modular robots that are customized from given modules for a specific task. This work presents an algorithm for proposing a module composition that is optimal with respect to performance metr…
Safe motion planning requires that a vehicle reaches a set of safe states at the end of the planning horizon. However, safe states of vehicles have not yet been systematically defined in the literature, nor does a computationally efficient way to obtain them for online motion planning exist. To tack…
This paper presents a hierarchical path planner consisting of two stages: a global planner that uses workspace information to create collision-free paths for the robot end-effector to follow, and multiple local planners running in parallel that verify the paths in the configuration space by expandin…
Model-based design of robotic systems has many advantages, among them faster development cycles and reduced costs due to early detections of design flaws. Approximate models are sufficient for many classical robotic applications; however, they no longer suffice for safety-critical applications. For…
We address the problem of overapproximative short-term prediction of arm movement, for safe human-robot co-existence. A trajectory planner for a robot manipulator which formally verifies non-collision requires fast prediction of human future occupancy, which must be accurate yet still account for al…
We consider the global tracking control problem of robots with elastic joints. Even if joint elasticity introduces beneficial features for modern applications which require physically resilient and safer robots that can interact with the environment or humans, it challenges the achievable control pe…
Commercially available robots cannot always be adapted to arbitrary tasks or environments, particularly when the task would exceed the kinematic or dynamic limits of the robot. Modular robots offer a solution to this problem, since they can be reconfigured in various ways from a set of modules. The…
Mobile robots operating in a shared environment with pedestrians are required to move provably safe to avoid harming pedestrians. Current approaches like safety fields use conservative obstacle models for guaranteeing safety, which leads to degraded performance in populated environments. In this pap…
This paper presents a time-efficient, task-based configuration synthesis algorithm for modular robot manipulators. One of the main challenges in modular manipulators is to find possible combinations of modules that are able to complete given tasks while avoiding obstacles in the environment. Most st
Human motion is fast and hard to predict. To implement a provably safe collision-avoidance strategy for robots in collaborative spaces with humans, an overapproximative prediction of the occupancy of the human is required, which needs to be calculated faster than real time. We present a method for c…
We address the problem of controlling modular robot manipulators. The challenge of modular-robot control is that the overall system dynamics are unknown due to its flexible composition from given modules. Most previous work has faced this problem by designing decentralized controllers. Simple decent…
We address the problem of verifying motion plans for aerial robots in uncertain and partially-known environments. Thereby, the initial state of the robot is uncertain due to errors from the state estimation and the motion is uncertain due to wind disturbances and control errors caused by sensor nois…
A failsafe control strategy is presented for online safety certification of robot movements in a collaborative workspace with humans. This approach plans, predicts and uses formal guarantees on reachable sets of a robot arm and a human obstacle to verify the safety and feasibility of a trajectory in…