skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Thursday, October 10 until 2:00 AM ET on Friday, October 11 due to maintenance. We apologize for the inconvenience.


Title: HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration
The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial two-finger-gripper robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to a target commercial robot.  more » « less
Award ID(s):
2024594
NSF-PAR ID:
10366296
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Conference on Robot Learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Learning a robot motor skill from scratch is impractically slow; so much so that in practice, learning must typically be bootstrapped using human demonstration. However, relying on human demonstration necessarily degrades the autonomy of robots that must learn a wide variety of skills over their operational lifetimes. We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation. We demonstrate the use of motion planners to bootstrap motor skills in two complex object manipulation scenarios with different policy representations: opening a drawer with a dynamic movement primitive representation, and closing a microwave door with a deep neural network policy. We also show how our method can bootstrap a motor skill for the challenging dynamic task of learning to hit a ball off a tee, where a kinematic plan based on treating the scene as static is insufficient to solve the task, but sufficient to bootstrap a more dynamic policy. In all three cases, our method is competitive with human-demonstrated initialization, and significantly outperforms starting with a random policy. This approach enables robots to to efficiently and autonomously learn motor policies for dynamic tasks without human demonstration. 
    more » « less
  2. Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot. 
    more » « less
  3. Rotation manipulation tasks are a fundamental component of manipulation, however few benchmarks directly measure the limits of a hand's ability to rotate objects. This paper presents two benchmarks for quantitatively measuring the rotation manipulation capabilities of two-fingered hands. These benchmarks exists to augment the Asterisk Test to consider rotation manipulation ability. We propose two benchmarks: the first assesses a hand's limits to rotate objects clockwise and counterclockwise with minimal translation, and the second assesses how rotation manipulation impacts a hand's in-hand translation performance. We demonstrate the utility of these rotation benchmarks using three generic robot hand designs: 1) an asymmetrical two-linked versus one-linked gripper (2v1), 2) a symmetrical two-linked gripper (2v2), and 3) a symmetrical three-linked gripper (3v3). We conclude with a brief comparison between the hand designs and a observations about contact point selection for manipulation tasks, informed from our benchmark results. 
    more » « less
  4. Humans use all surfaces of the hand for contact-rich manipulation. Robot hands, in contrast, typically use only the fingertips, which can limit dexterity. In this work, we leveraged a potential energy–based whole-hand manipulation model, which does not depend on contact wrench modeling like traditional approaches, to design a robotic manipulator. Inspired by robotic caging grasps and the high levels of dexterity observed in human manipulation, a metric was developed and used in conjunction with the manipulation model to design a two-fingered dexterous hand, the Model W. This was accomplished by simulating all planar finger topologies composed of open kinematic chains of up to three serial revolute and prismatic joints, forming symmetric two-fingered hands, and evaluating their performance according to the metric. We present the best design, an unconventional robot hand capable of performing continuous object reorientation, as well as repeatedly alternating between power and pinch grasps—two contact-rich skills that have often eluded robotic hands—and we experimentally characterize the hand’s manipulation capability. This hand realizes manipulation motions reminiscent of thumb–index finger manipulative movement in humans, and its topology provides the foundation for a general-purpose dexterous robot hand.

     
    more » « less
  5. Grasp is an integral part of manipulation actions in activities of daily living and programming by demonstration is a powerful paradigm for teaching the assistive robots how to perform a grasp. Since finger configuration and finger force are the fundamental features that need to be controlled during a grasp, using these variables is a natural choice for learning by demonstration. An important question then becomes whether the existing grasp taxonomies are appropriate when one considers these modalities. The goal of our paper is to answer this question by investigating grasp patterns that can be inferred from a static analysis of the grasp data, as the object is securely grasped. Human grasp data is measured using a newly developed data glove. The data includes pressure sensor measurements from eighteen areas of the hand, and measurements from bend sensors placed at finger joints. The pressure sensor measurements are calibrated and mapped into force by employing a novel data-driven approach. Unsupervised learning is used to identify patterns for different grasp types. Multiple clustering algorithms are used to partition the data. When the results are taken in aggregate, 25 human grasp types are reduced to 9 different clusters. 
    more » « less