Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human’s prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging — but the autonomous car actually intends to merge — then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through proof-of-concept simulations, testing our method’s predictions against those of real users, and experiments on a real-world interactive driving dataset.

We can make it easier for disabled users to control assistive robots by mapping the user's low-dimensional joystick inputs to high-dimensional, complex actions. Prior works learn these mappings from human demonstrations: a non-disabled human either teleoperates or kinesthetically guides the robot arm through a variety of motions, and the robot learns to reproduce the demonstrated behaviors. But this framework is often impractical — disabled users will not always have access to external demonstrations! Here we instead learn diverse teleoperation mappings without either human demonstrations or pre-defined tasks. Under our unsupervised approach the robot first optimizes for object state entropy: i.e., the robot autonomously learns to push, pull, open, close, or otherwise change the state of nearby objects. We then embed these diverse, object-oriented behaviors into a latent space for real-time control: now pressing the joystick causes the robot to perform dexterous motions like pushing or opening. We experimentally show that — with a best-case human operator — our unsupervised approach actually outperforms the teleoperation mappings learned from human demonstrations, particularly if those demonstrations are noisy or imperfect. But our user study results were less clear-cut: although our approach enabled participants to complete tasks more quickly and with fewer changes of direction, users were confused when the unsupervised robot learned unexpected behaviors. See videos of the user study here: https://youtu.be/BkqHQjsUKDg

When robots interact with human partners, often these partners change their behavior in response to the robot. On the one hand this is challenging because the robot must learn to coordinate with a dynamic partner. But on the other hand - if the robot understands these dynamics - it can harness its own behavior, influence the human, and guide the team towards effective collaboration. Prior research enables robots to learn to influence other robots or simulated agents. In this paper we extend these learning approaches to now influence humans. What makes humans especially hard to influence is that - not only do humans react to the robot - but the way a single user reacts to the robot may change over time, and different humans will respond to the same robot behavior in different ways. We therefore propose a robust approach that learns to influence changing partner dynamics. Our method first trains with a set of partners across repeated interactions, and learns to predict the current partner's behavior based on the previous states, actions, and rewards. Next, we rapidly adapt to new partners by sampling trajectories the robot learned with the original partners, and then leveraging those existing behaviors to influence the new partner dynamics. We compare our resulting algorithm to state-of-the-art baselines across simulated environments and a user study where the robot and participants collaborate to build towers. We find that our approach outperforms the alternatives, even when the partner follows new or unexpected dynamics. Videos of the user study are available here: https://youtu.be/1YsWM8An18g

A quadcopter is an under-actuated system with only four control inputs for six degrees of freedom, and yet the human control of a quadcopter is simple enough to be learned with some practice. In this work, we consider the problem of human control of a multiple quadcopters system to transport a cable-suspended payload. The coupled dynamics of the system, due to the inherent physical constraints, is used to develop a leader-follower architecture where the leader quadcopter is controlled directly by a human operator and the followers are controlled with the proposed Payload Attitude Controller and Cable Attitude Controller. Experiments, where a human operator flew a two quadcopters system to transport a cable-suspended payload, were conducted to study the performance of proposed controller. The results demonstrated successful implementation of human control in these systems. This work presents the possibility of enabling manual control for on-the-go maneuvering of the quadcopter-payload system which motivates aerial transportation in the unknown environments.

We study the problem of collaborative transportation of cable-suspended payload using two quadcopters. While previous works on transportation using quadcopters emphasize more on autonomous control and generating complex trajectory, in this paper a master-slave strategy is implemented where the master quadcopter is controlled by human and the slave quadcopter tries to stabilize the oscillations of the payload. Two quadcopters with a cable-suspended payload system is under-actuated with coupled dynamics and hence, manual control is difficult. We use Lagrangian mechanics on a manifold for deriving equations of motion and apply variation based linearization to linearize the system. We designed a Lyapunov based controller to minimize the oscillations of the payload while transportation, leading to an easier manual control of master quadcopter.