9 accepted papers
Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are long-term and involve a set of skill primitives. Thus it is crucial to hav…
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose-only demonstrations and thus only skills with spa…
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning, etc. DONs map an RGB image depicting an object in…
Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensi
While manipulation skills such as picking, inserting and placing were hard coded in classical setups, it is now widely understood that this leads to poor flexibility and that more general skill formulations are required to ensure re-usability in new scenarios. We thus adopt a skill-centric approach
Coordinating multiple agents, such as mobile robots, with shared resources, such as common battery charging stations, is a highly relevant but still challenging decision problem. Traditionally, the motion and task planning of multi-agent systems are tackled by either designing ad-hoc decision rules
Guaranteeing safe constraint satisfaction in nonlinear control systems with uncertainty remains a major challenge for control. The most successful control method handling constraints under uncertainty has without doubt been model predictive control (MPC). In particular, recent sampling-based MPC met…
Coordinating a team of robots to fulfill a common task is still a demanding problem. This is even more the case when considering uncertainty in the environment, as well as temporal dependencies within the task specification. A multi-robot cooperation from a single goal specification requires mechani…
We present an efficient approach to plan action sequences for a team of robots from a single finite LTL mission specification. The resulting execution strategy is proven to solve the given mission with minimal team costs, e.g., with shortest execution time. For planning, an established graph-based s…