We present a novel approach for generating candidate handover positions for human receivers. The object handover problem involves several variables and is under-constrained. The sheer number of possibilities makes it nontrivial to find a good object handover position for existing algorithms. To address this problem, we introduce the use of a custom biomechanical model in a novel algorithm for generating a handover position. The biomechanical model serves as a tool for approximating the static strength a human receiver needs to exert in order to receive an object. A mobile manipulator can use the output of our algorithm when calculating a target position for handing over an object to a human. We show preliminary results involving electromyography data and joint moment values, as well as a proof-of-concept implementation with a mobile manipulator to fetch a toolbox to a receiver.

Supervision and teleoperation of high degree-of-freedom robots is a complex task due to environmental constraints such as obstacles and limited communication, as well as task specific requirements such as using more than one end-effector at the same time. In this work we present a supervision and teleoperation framework that allows an operator to see the surroundings of a robot in 3D, make necessary adjustments for a dual or single arm manipulation task, preview the task in simulation before execution, and finally execute the task on a real robot. The framework has been applied to the valve turning task of the DARPA Robotics Challenge on the PR2, Hubo2+, and DRCHubo robots.

In this paper, we report lessons learned through the design of a framework for teleoperating a humanoid robot to perform a manipulation task. We present a software framework for cooperative traded control that enables a team of operators to control a remote humanoid robot over an unreliable communications link. The framework produces statically-stable motion trajectories that are collision-free and respect end-effector pose constraints. After operator confirmation, these trajectories are sent over the data link for execution on the robot. Additionally, we have defined a clear operational procedure for the operators to manage the teleoperation task. We applied our system to the valve turning task in the DARPA Robotics Challenge (DRC). Our framework is able to perform reliably and is resilient to unreliable network conditions, as we demonstrate in a set of test runs performed remotely over the internet. We analyze our approach and discuss lessons learned which may be useful for others when designing such a system.

Many different robot learning from demonstration methods have been applied and tested in various environments recently. Representation of learned plans, tasks and policies often depends on the technique due to method-specific parameters. An agent that is able to switch between representations can apply its knowledge to different algorithms. This flexibility can be useful for a human teacher when training the agent. In this work we present a process to convert learned policies with two specific methods, Confidence-Based Autonomy (CBA) and Interactive Reinforcement Learning (Int-RL), to each other. Our finding suggests that it is possible for an agent to learn a policy with either CBA or Int-RL method and execute the task with the other with the benefit of previously learned knowledge.

Research on robot learning from demonstration has seen significant growth in recent years, but existing evaluations have focused exclusively on algorithmic performance and not on usability factors, especially with respect to naïve users. Here we present findings from a comparative user study in which we asked non-experts to evaluate three distinctively different robot learning from demonstration algorithms - Behavior Networks, Interactive Reinforcement Learning, and Confidence Based Autonomy. Participants in the study showed a preference for interfaces where they controlled the robot directly (teleoperation and guidance) instead of providing retroactive feedback for past actions (reward and correction). Our results show that the best policy performance in most metrics was achieved using the Confidence Based Autonomy algorithm.

Most human interactions with the environment depend on our ability to navigate freely and to use our hands and arms to manipulate objects. Developing natural means of controlling these abilities in humanoid robots can significantly broaden the usability of such platforms. An ideal interface for humanoid robot teleoperation will be inexpensive, person-independent, require no wearable equipment, and will be easy to use, requiring little or no user training.

The Interactive Reinforcement Learning algorithm enables a human user to train a robot by providing rewards in response to past actions and anticipatory guidance to guide the selection of future actions. Past work with software agents has shown that incorporating user guidance into the policy learning process through Interactive Reinforcement Learning significantly improves the policy learning time by reducing the number of states the agent explores. We present the first study of Interactive Reinforcement Learning in real-world robotic systems. We report on four experiments that study the effects that teacher guidance and state space size have on policy learning performance. We discuss modifications made to apply Interactive Reinforcement Learning to a real-world system and show that guidance significantly reduces the learning rate, and that its positive effects increase with state space size.

Robot learning from demonstration focuses on algorithms that enable a robot to learn a policy from demonstrations performed by a teacher, typically a human expert. This paper presents an experimental evaluation of two learning from demonstration algorithms, Interactive Reinforcement Learning and Behavior Networks. We evaluate the performance of these algorithms using a humanoid robot and discuss the relative advantages and drawbacks of these methods with respect to learning time, number of demonstrations, ease of implementation and other metrics. Our results show that Behavior Networks rely on a greater degree of domain knowledge and programmer expertise, requiring very precise definitions for behavior pre- and post-conditions. By contrast Interactive RL requires a relatively simple implementation based only on the robot's sensor data and actions. However, Behavior Networks leverage the pre-coded knowledge to effectively reduce learning time and the required number of human interactions to learn the task.