As the application of probabilistic models in robotic applications increases, a systematic robot control approach considering the effects of uncertainty becomes indispensable. Inspired by human sensorimotor findings, in this paper, we study the stochastic optimal control problem with high-order cost statistics in order to synthesize uncertainty-dependent actions in robotic scenarios with multiple uncertainty sources. We present locally optimal risk-sensitive and cost-cumulant solutions for settings with nonlinear dynamics, multiple additive uncertainty sources, and nonquadratic costs. The influence of each uncertainty source on the cost can be individually parameterized offering additional flexibility in the control design. We further analyze the case in which the static uncertain parameters are involved. The simulations of several linear and nonlinear settings with nonquadratic costs and an experiment on a real robotic platform validate our approach and illustrate its peculiarities.

For seamless physical human-robot interaction (pHRI), estimating human intention is essential. Most system identification approaches to pHRI model the human as a black box without prior assumptions about the underlying behavioral structure. However, integrating a priori knowledge about behavioral characteristics of the human provides superior prediction performance. In this work we present a novel method for human behavior prediction during physical interaction that incorporates an empirically supported human motor control model. The arm dynamics of the human are modeled as a mechanical impedance that follows a latent desired trajectory. We adopt a Bayesian perspective setting Gaussian Process (GP) priors on impedance parameters and the desired trajectory, which allows regression about human behavior from observed trajectories and interaction forces. The proposed impedance-based GP model is validated in simulation and in an experiment with human participants to demonstrate its prediction performance and generalization capability.

As the application of probabilistic models in robotic applications increases, the necessity of a systematic robot-control method that considers the effects of multiple uncertainty sources becomes more evident. Motivated by human sensorimotor findings, in this work we study the stochastic locally optimal feedback control problem with high-order cost statistics where dynamics have multiple additive noise sources and cost variability produced by each uncertainty source is evaluated marginally. We present risk-sensitive and cost-cumulant solutions for this problem for non-linear dynamics and non-quadratic costs. Locally optimal solutions are found by iteratively performing a linear quadratic approximation around a nominal trajectory, solving the local problem and updating the trajectory until convergence. Simulation results of a point mass robot and a two-link manipulator validate the applicability of the proposed approach and illustrate its peculiarities.

Intuitive and effective physical assistance is an essential requirement for robots sharing their workspace with humans. Application domains reach from manufacturing and service robotics via rehabilitation and mobility aids to education and training. In this context, assistance based on human behavior anticipation has shown superior performance in terms of human effort minimization. However, when a robot's expectations mismatch a human intentions, undesired interaction forces appear incurring safety risks and discomfort. Human behavior prediction is, therefore, a crucial issue: It enables effective anticipation but potentially produces disagreements when prediction errors occur. In this paper, we present a novel control scheme for anticipatory haptic assistance where robot behavior adapts to prediction uncertainty. Following a data-driven stochastic modeling approach, robot assistance is synthesized solving a risk-sensitive optimal control problem, where the cost function and plant dynamics are affected by model uncertainty. The proposed approach is objectively and subjectively evaluated in an experiment with human users. Results indicate that our method outperforms other assistive control approaches in terms of perceived helpfulness and human effort minimization.

It is well-known that physical robotic assistance to humans is significantly enhanced by including human behavior anticipation into robot planning and control. The challenge arises when the human goal/plan is uncertain or unknown to the robot. In this paper we propose a novel control scheme which dynamically selects between a model-based and a model-free strategy depending on the level of disagreement between the human and the robot. The disagreement is measured in terms of the interaction force. A task specific model-based controller is selected when the human's motion intention coincides with the robot's goal. A model-free control scheme based on the human force as motion prediction source is selected in case of disagreement and when the human goal/plan is unknown. The benefits of this approach are demonstrated in a human user study on human-robot cooperative object transport through a 2D maze in virtual reality.

Manipulation tasks are a great challenge for robots due to the uncertainty arising from unstructured environments. In this paper we propose a novel control scheme for contact tasks based on risk-sensitive optimal feedback control. It provides a systematic approach to adjust the trade-off between motion and force control under uncertainty. Following a previously acquired task model, the proposed approach provides both a variable stiffness solution and a motion reference adaptation. This control scheme achieves increased adaptability under previously unseen environmental variability. An implementation on a robotic manipulator validates the applicability and adaptability of the proposed control approach in two different manipulation tasks.

While human behavior prediction can increase the capability of a robotic partner to generate anticipatory behavior during physical human robot interaction (pHRI), predictions in uncertain situations can lead to large disturbances for the human if they do not match the human intentions. In this paper we present a novel control concept in which the assistive control parameters are adapted to the uncertainty in the sense that a the robot takes a more or less active role depending on its confidence in the human behavior prediction. The approach is based on risk-sensitive optimal feedback control. The human behavior is modeled using probabilistic learning methods and any unexpected disturbance is considered as a source of noise. The proposed approach is validated in situations with different uncertainties, process noise and risk-sensitivities in a tow- Degree-of-Freedom virtual reality experiment.

Goal-directed physical assistance to the human is one of the most challenging problems in the area of human-robot interaction. Planning and learning from demonstration represent two conceptually different approaches to achieve goal-directed behavior. Here we examine the properties of a planning-based and a learning-based approach in the context of physical robotic assistance for the prototypical task of cooperative object maneuvering. In order to exploit the complementary strengths of planning and learning-based approaches we derive three novel synergy strategies. The algorithms are experimentally evaluated in a human user study in a planar virtual-reality scenario and in a proof-of-concept study with a human-sized mobile robot with two 7DoF arms. The results show that combinations of planning and learning algorithms are superior over the individual approaches.

Proactive physical robotic assistance in the presence of human prediction uncertainty is a very challenging control problem. In this paper we propose a risk-sensitive optimal feedback controller for physical assistance that autonomously adapts the robot's behavior even during unknown situations. Using a probabilistic model to represent the cooperative task execution behavior and modeling the human as a source of process noise in the system, the proposed assistive controller proactively contributes to the task anticipating the human motion. Estimating online the current level of disagreement and prediction uncertainty, the assistive controller consequently calculates the optimal task contribution providing higher adaptability. A psychological evaluation compares different assistive control strategies in a virtual scenario using a two-Degree-of-Freedom haptic experimental setup. Results show that considering the current level of disagreement enhances the performance of the controller in terms of helpfulness and human effort minimization.

Natural language interaction between humans and robots is a very challenging topic, especially when it refers to motion descriptions in a certain environment. This problem is particularly relevant during physical human-robot interaction, e.g. in cooperative transportation tasks, where the partners' physical coupling requires an agreement on the way to follow. Understanding in depth the link between sentences, words, environmental properties and motions can deeply enhance the interaction between humans and robots. In this work, we propose a novel approach for learning relations and dependencies between motion, natural language and environmental properties using parameterized left-to-right time-based Hidden Markov Models. A natural language model represents the link between language and motion symbols while the HMMs parameterization corresponds to the explicit influence on motions of both words and environmental features. The proposed PHMM approach parameterizes the output and the transition probabilities using a non-linear dependency estimation. The method is validated by learning and generating navigation primitives in a 2 Degrees-Of-Freedom (DoF) virtual scenario.

Anticipatory behavior based on the human behavior prediction enables the robot to improve the quality of its assistance in physical human-robot interaction (pHRI). However, predictions are partly afflicted with high uncertainties originating from the intrinsic variability in human behavior and the influence of the environment, requiring an attitude negotiation among partners. In this paper, we propose a novel control approach that dynamically adapts the robot's attitude to the disagreement level and the environmental situation in real time facilitating the negotiation between the human and the robot. The approach is based on risk-sensitive optimal feedback control. The adaptive design of the robot's attitude is realized through a dynamical changing risk-sensitivity parameter. The proposed approach is experimentally validated in a cooperative transport scenario in a two-dimensional visuo-haptic virtual environment.

Physical cooperation with humans greatly enhances the capabilities of robotic systems when leaving standardized industrial settings. Our novel cognition-enabled control framework presented in this paper enables a robotic assistant to enrich its own experience by acquisition of human task knowledge during joint manipulation. Our robot incrementally learns semantic task structures during joint task execution using hierarchically clustered Hidden Markov Models. A semantic labeling of recognized task segments is acquired from the human partner through speech. After a small number of repetitions, the robot uses an anticipated task progress to generate a feed-forward set point for an admittance feedback control scheme. This paper describes the framework and its implementation on a mobile bi-manual platform. The evolution of the robot's task knowledge is presented and discussed. Finally, the cooperation quality is measured in terms of the robot's task contribution.