9 accepted papers
In this paper, we define the problem of human-robot collaboration as a combined task and motion planning problem which is extended to the multi-agent case (human and robot). Our proposed approach allows us to explicitly take into account ergonomic cost, synchrony and concurrency of behavior in an op…
Learning object models in the wild from natural human interactions is an essential ability for robots to perform general tasks. In this paper we present a robocentric multimodal dataset addressing this key challenge. Our dataset focuses on interactions where the user teaches new objects to the robot…
Joint symbolic and geometric planning is one of the core challenges in robotics. We address the problem of multi-agent cooperative manipulation, where we aim for jointly optimal paths for all agents and over the full manipulation sequence. This joint optimization problem can be framed as a logic-geo…
In human-robot collaboration the robot's behavior impacts the worker's safety, comfort and acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human ki…
We present a novel method to learn human preferences during, and for, the execution of concurrent joint humanrobot tasks. We consider tasks realized by a team of a human operator and a robot helper that should adapt to the human's task execution preferences. Different human operators can have differ…
In human-robot collaboration, multi-agent domains, or single-robot manipulation with multiple end-effectors, the activities of the involved parties are naturally concurrent. Such domains are also naturally relational as they involve objects, multiple agents, and models should generalize over objects
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…
This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approaches such as learning from demonstration, learning from feedback and knowledge tran…
We consider the problem of learning from complex sequential demonstrations. We propose to analyze demonstrations in terms of the concurrent interaction phases which arise between pairs of involved bodies (hand-object and object-object). These interaction phases are the key to decompose a full demons…