Motivated by recent advances in Deep Learning for robot control, this paper considers two learning algorithms in terms of how they acquire demonstrations. "Human-Centric" (HC) sampling is the standard supervised learning algorithm, where a human supervisor demonstrates the task by teleoperating the robot to provide trajectories consisting of state-control pairs. "Robot-Centric" (RC) sampling is an increasingly popular alternative used in algorithms such as DAgger, where a human supervisor observes the robot executing a learned policy and provides corrective control labels for each state visited. RC sampling can be challenging for human supervisors and prone to mislabeling. RC sampling can also induce error in policy performance because it repeatedly visits areas of the state space that are harder to learn. Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error. We compare HC and RC using a grid world and a physical robot singulation task, where in the latter the input is a binary image of a connected set of objects on a planar worksurface and the policy generates a motion of the gripper to separate one object from the rest. We observe in simulation that for linear SVMs, policies learned with RC outperformed those learned with HC but that with deep models this advantage disappears. We also find that with RC, the corrective control labels provided by humans can be highly inconsistent. We prove there exists a class of examples where in the limit, HC is guaranteed to converge to an optimal policy while RC may fail to converge.
Standard techniques for providing demonstrations to a robot are teleoperation, kinesthetic and waypoint specification @cite_19 @cite_0 @cite_16 . Kinesthetic teaching is defined as moving the robot body via a human exerting force on the robot itself. Teleoperation uses an interface such as a joystick or video game controller to control the position of the robot end effector. Waypoint specification, or Keyframes, has a human select positions in the space the robot needs to visit. These methods are forms of HC sampling because the human guides the robot through the task. We look specifically at teleoperation and compare it to RC's form of retroactive feedback.
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The field of robot Learning from Demonstration (LfD) makes use of several input modalities for demonstrations (teleoperation, kinesthetic teaching, marker- and vision-based motion tracking). In this paper we present two experiments aimed at identifying and overcoming challenges associated with using teleoperation as an input modality for LfD. Our first experiment compares kinesthetic teaching and teleoperation and highlights some inherent problems associated with teleoperation; specifically uncomfortable user interactions and inaccurate robot demonstrations. Our second experiment is focused on overcoming these problems and designing the teleoperation interaction to be more suitable for LfD. In previous work we have proposed a novel demonstration strategy using the concept of keyframes, where demonstrations are in the form of a discrete set of robot configurations. Keyframes can be naturally combined with continuous trajectory demonstrations to generate a hybrid strategy. We perform user studies to evaluate each of these demonstration strategies individually and show that keyframes are intuitive to the users and are particularly useful in providing noise-free demonstrations. We find that users prefer the hybrid strategy best for demonstrating tasks to a robot by teleoperation. We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a Human–Robot Interaction perspective. Our approach—Keyframe-based Learning from Demonstration (KLfD)—takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.