23 accepted papers
Transferring human skills to robots through learning from demonstrations has been an important topic in the robotics community, and many models have been developed for learning and adapting such skills. Among them, nonparametric representations are an appealing choice, since nonparametric solutions …
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
Learning from demonstration (LfD) offers an intuitive alternative to manual coding by leveraging natural human behavior, while Human-Robot Interaction (HRI) provides an intuitive means to refine and adapt learned skills. Among interaction modalities, physical contact is a natural and effective way t
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
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
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…
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…
Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills. Among these, probabilistic approaches have earned a relevant place, owing to the…
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…
Collaborative robots offer increased interaction capabilities at relatively low cost but in contrast to their industrial counterparts they inevitably lack precision. Moreover, in addition to the robots' own imperfect models, day-to-day operations entail various sources of errors that despite being s…
Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between local and remote workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g., picking objects with different poses on the two
In the context of imitation learning, several approaches have been developed so as to transfer human skills to robots, with demonstrations often represented in Cartesian or joint space. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in man…
Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different conditions? A potential solution is to endow robots…
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive mea…
Motivated by the desire to have robots physically present in human environments, in recent years we have witnessed an emergence of different approaches for learning active compliance. Some of the most compelling solutions exploit a minimal intervention control principle, correcting deviations from a…
Programming by demonstration has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized Gaussian mixture model (TP-GMM)…
In the context of learning from demonstration, human examples are usually imitated in either Cartesian or joint space. However, this treatment might result in undesired movement trajectories in either space. This is particularly important for motion skills such as striking, which typically imposes m…
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which ta…
As many imitation learning algorithms focus on pure trajectory generation in either Cartesian space or joint space, the problem of considering competing trajectory constraints from both spaces still presents several challenges. In particular, when perturbations are applied to the robot, the underlyi…
In imitation learning, multivariate Gaussians are widely used to encode robot behaviors. Such approaches do not provide the ability to properly represent end-effector orientation, as the distance metric in the space of orientations is not Euclidean. In this paper, we present an extension of common i
Very often, when addressing the problem of human-robot skill transfer in task space, only the Cartesian position of the end-effector is encoded by the learning algorithms, instead of the full pose. However, orientation is just as important as position, if not more, when it comes to successfully perf…