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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Human-in-the-loop manipulation is useful in when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator's hand as an input device can provide an intuitive control method but requires mapping between pose spaces which may not be similar. We propose a low-dimensional and continuous teleoperation subspace which can be used as an intermediary for mapping between different hand pose spaces. We present an algorithm to project between pose space and teleoperation subspace. We use a non-anthropomorphic robot to experimentally prove that it is possible for teleoperation subspaces to effectively and intuitively enable teleoperation. In experiments, novice users completed pick and place tasks significantly faster using teleoperation subspace mapping than they did using state of the art teleoperation methods. Kinesthetic teaching is an approach to providing demonstrations to a robot in Learning from Demonstration whereby a human physically guides a robot to perform a skill. In the common usage of kinesthetic teaching, the robot's trajectory during a demonstration is recorded from start to end. In this paper we consider an alternative, keyframe demonstrations, in which the human provides a sparse set of consecutive keyframes that can be connected to perform the skill. We present a user-study (n = 34) comparing the two approaches and highlighting their complementary nature. The study also tests and shows the potential benefits of iterative and adaptive versions of keyframe demonstrations. Finally, we introduce a hybrid method that combines trajectories and keyframes in a single demonstration. Abstract Internet telerobotics has emerged in recent decade with direct control and supervisory control as the main teleoperation paradigms. Both paradigms, however, are difficult to use on applications operating in the unknown and dynamic real world, while they do not provide adequate feeling of interaction or a human-friendly control interface to human operator. This paper proposes a novel interactive control (i.e., active supervisory control) paradigm: telecommanding, which is used for Internet-based wheeled robot teleoperation. Telecommanding involves two parts: basic telecommanding using joystick commands, and advanced telecommanding using linguistic commands. Each joystick or linguistic command is designed to perform an independent task and is defined with multiple events (non-time action references), and the corresponding response functions. This event-driven mechanism enables the robot to deliberately respond to expected events while to reactively respond to unexpected events. Assisted by up-to-date media streaming technologies, telecommanding can help a novice operator to easily control an Internet robot navigating in an unknown and dynamic real world. Experiments, including an Internet-based teleoperation test over 1500 km from Beijing to Hong Kong, demonstrate the promising performance. 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.