We explore beyond existing work in learning from demonstration by asking the question: “Can robots learn to guide?”, that is, can a robot autonomously learn an instructional policy from expert demonstration and use it to instruct humans in executing complex task? As a solution, we propose learning of instructional policy (πI) that maps the state to an instruction for a human. To learn πI, we define action primitives that addresses the challenge of mapping continuous state action trajectories to human parse-able instructions. Action primitives are demonstrated to be very effective in automatic segmentation of demonstration trajectories into fewer repetitive and reusable segments, and a highly scalable approach in comparison to the existing state-of-the art. Finally, we construct a non-generic policy model as a generative model for instructional policies to generate instruction for an entire task. With few modifications, the proposed model is demonstrated to perform autonomous execution of complex truck loading task on hydraulic actuated scaled excavator robot. Guidance approach is tested based on a controlled group study involving 75 participants, who learn to perform the same task.

Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while automatic control providing assistance to improve task performance. Given a task with specific subgoals, execution of a task using blended shared control involves predicting the operator's intent of subgoal transitions and deciding the blending weights for inputs from the human operator and automatic control. In this paper we address the problem of subgoal adjustment in blended shared control which is typically initiated by the operator's intent and necessary to sustain the shared control performance for changing subgoal conditions. First, we provide a method to predict operator's intent of visiting a subgoal. Based on intent prediction, we propose a method for subgoal adjustment where the adjustment is encoded by a hyperrectangle. The volume of the hyper-rectangle is obtained by using a hyperbolic slope transition function which is based on the distance between subgoals. The adjustment actions within the hyper-rectangle are facilitated by a skill-weighted action integral that takes into consideration the skill level of the operator. The approach is tested on a scaled hydraulic excavator platform with multiple novice operators and a skilled operator. Experimental results are presented and discussed.

We consider the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements. We show that layering a dynamical systems prior over temporal evolution of weights of a kernel model is a valid approach to spatiotemporal modeling that does not necessarily require the design of complex nonstationary kernels. Furthermore, we show that such a predictive model can be utilized to determine sensing locations that guarantee that the hidden state of the phenomena can be recovered with very few measurements. We provide sufficient conditions on the number and spatial location of samples required to guarantee state recovery, and provide a lower bound on the minimum number of samples required to robustly infer the hidden states. Our approach outperforms existing methods in numerical experiments.