This paper introduces the Proactive Assistance through action-Completion Estimation (PACE) framework, designed to enhance human-robot collaboration through real-time monitoring of human progress. PACE incorporates a novel method that combines Dynamic Time Warping (DTW) with correlation analysis to track human task progression from hand movements. PACE trains a reinforcement learning policy from limited demonstrations to generate a proactive assistance policy that synchronizes robotic actions with human activities, minimizing idle time and enhancing collaboration efficiency. We validate the framework through user studies involving 12 participants, showing significant improvements in interaction fluency, reduced waiting times, and positive user feedback compared to traditional methods.

This paper proposes a task planning framework for collaborative Human-Robot scenarios, specifically focused on assembling complex systems such as furniture. The human is characterized as an uncontrollable agent, implying for example that the agent is not bound by a pre-established sequence of actions and instead acts according to its own preferences. Meanwhile, the task planner computes reactively the optimal actions for the collaborative robot to efficiently complete the entire assembly task in the least time possible.We formalize the problem as a Discrete Event Markov Decision Problem (DE-MDP), a comprehensive framework that incorporates a variety of asynchronous behaviors, human change of mind, and failure recovery as stochastic events. Although the problem could theoretically be addressed by constructing a graph of all possible actions, such an approach would be constrained by computational limitations. The proposed formulation offers an alternative solution utilizing Reinforcement Learning to derive an optimal policy for the robot. Experiments were conducted both in simulation and on a real system with human subjects assembling a chair in collaboration with a 7-DoF manipulator.

Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. In this article, we propose a black-box model based on Gaussian process (GP) regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, calledLagrangian Inspired Polynomial(LIP) kernel. The LIP kernel is based on two main ideas. First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system. The GP prior on the inverse dynamics components is derived from those on the energies by applying the properties of GPs under linear operators. Second, as regards the energy prior definition, we prove a polynomial structure of the kinetic and potential energy, and we derive a polynomial kernel that encodes this property. As a consequence, the proposed model allows also to estimate the kinetic and potential energy without requiring any label on these quantities. Results on simulation and on two real robotic manipulators, namely a 7 DOF Franka Emika Panda, and a 6 DOF MELFA RV4FL, show that the proposed model outperforms state-of-the-art black-box estimators based both on Gaussian processes and neural networks in terms of accuracy, generality, and data efficiency. The experiments on the MELFA robot also demonstrate that our approach achieves performance comparable to fine-tuned model-based estimators, despite requiring less prior information. The code of the proposed model is publicly available.

In this paper, we investigate the use of ModelBased Reinforcement Learning (MBRL) for pick-and-throw tasks, which is a variation of the pick-and-place task. In pick-and-throw applications, objects are thrown instead of placed to increase time efficiency and enlarge the workspace reachable by the robot. The effectiveness of analytical methods for this task is limited by the complex system dynamics and the disturbances due to communication and control. Recently, the application of data-driven solutions, in particular Model-Free Reinforcement Learning (MFRL), has attracted attention. In this paper, we explore the use of a MBRL algorithm to solve the pick-and-throw task. Experiments carried out in simulation show the potentialities of our approach, in terms of accuracy and data efficiency, both with respect to analytical methods and a MFRL approach.