Teaching Robots to Grasp Like Humans: An Interactive Approach

• URL: http://arxiv.org/abs/2110.04534v1
• Date: Sat, 9 Oct 2021 10:27:50 GMT
• Title: Teaching Robots to Grasp Like Humans: An Interactive Approach
• Authors: Anna M\'esz\'aros, Giovanni Franzese, and Jens Kober
• Abstract summary: This work investigates how the intricate task of grasping may be learned from humans based on demonstrations and corrections. Rather than training a person to provide better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration.
• Score: 3.3836709236378746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work investigates how the intricate task of grasping may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement, orientation, and gripper width) have to be demonstrated at once. Rather than training a person to provide better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task. The presented framework learns the desired movement dynamics based on the current Cartesian Position with Gaussian Processes (GP), resulting in a reactive, time-invariant policy. Using GPs also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization. The experimental evaluation of the framework is carried out on a Franka-Emika Panda.

Related papers

• You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations [38.835807227433335]
  Bimanual robotic manipulation is a long-standing challenge of embodied intelligence. We propose YOTO, which can extract and then inject patterns of bimanual actions from as few as a single binocular observation. YOTO achieves impressive performance in mimicking 5 intricate long-horizon bimanual tasks.
  arXiv  Detail & Related papers  (2025-01-24T03:26:41Z)
• RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation [36.43143326197769]
  Track-Any-Point (TAP) models isolate the relevant motion in a demonstration, and parameterize a low-level controller to reproduce this motion across changes in the scene configuration. We show this results in robust robot policies that can solve complex object-arrangement tasks such as shape-matching, stacking, and even full path-following tasks such as applying glue and sticking objects together.
  arXiv  Detail & Related papers  (2023-08-30T11:57:04Z)
• Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation [66.86987509942607]
  We evaluate how such a paradigm should be done in imitation learning. We consider a setting where the pretraining corpus consists of multitask demonstrations. We argue that inverse dynamics modeling is well-suited to this setting.
  arXiv  Detail & Related papers  (2023-05-26T14:40:46Z)
• HumanMAC: Masked Motion Completion for Human Motion Prediction [62.279925754717674]
  Human motion prediction is a classical problem in computer vision and computer graphics. Previous effects achieve great empirical performance based on an encoding-decoding style. In this paper, we propose a novel framework from a new perspective.
  arXiv  Detail & Related papers  (2023-02-07T18:34:59Z)
• Out-of-Dynamics Imitation Learning from Multimodal Demonstrations [68.46458026983409]
  We study out-of-dynamics imitation learning (OOD-IL), which relaxes the assumption to that the demonstrator and the imitator have the same state spaces. OOD-IL enables imitation learning to utilize demonstrations from a wide range of demonstrators but introduces a new challenge. We develop a better transferability measurement to tackle this newly-emerged challenge.
  arXiv  Detail & Related papers  (2022-11-13T07:45:06Z)
• Eliciting Compatible Demonstrations for Multi-Human Imitation Learning [16.11830547863391]
  Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. Natural human behavior has a great deal of heterogeneity, with several optimal ways to demonstrate a task. This mismatch presents a problem for interactive imitation learning, where sequences of users improve on a policy by iteratively collecting new, possibly conflicting demonstrations. We show that we can both identify incompatible demonstrations via post-hoc filtering, and apply our compatibility measure to actively elicit compatible demonstrations from new users.
  arXiv  Detail & Related papers  (2022-10-14T19:37:55Z)
• Visual Adversarial Imitation Learning using Variational Models [60.69745540036375]
  Reward function specification remains a major impediment for learning behaviors through deep reinforcement learning. Visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents. We develop a variational model-based adversarial imitation learning algorithm.
  arXiv  Detail & Related papers  (2021-07-16T00:15:18Z)
• Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration [8.57914821832517]
  We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration. Our method models imitation learning as a state estimation problem, with the state defined as the end-effector's pose. At test time, the end-effector moves to the estimated state through a linear path, at which point the original demonstration's end-effector velocities are simply replayed.
  arXiv  Detail & Related papers  (2021-05-13T16:36:55Z)
• Learning from Imperfect Demonstrations from Agents with Varying Dynamics [29.94164262533282]
  We develop a metric composed of a feasibility score and an optimality score to measure how useful a demonstration is for imitation learning. Our experiments on four environments in simulation and on a real robot show improved learned policies with higher expected return.
  arXiv  Detail & Related papers  (2021-03-10T07:39:38Z)
• Learning to Shift Attention for Motion Generation [55.61994201686024]
  One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories. We propose a motion generation model with extrapolation ability to overcome this problem.
  arXiv  Detail & Related papers  (2021-02-24T09:07:52Z)
• Reinforcement Learning with Supervision from Noisy Demonstrations [38.00968774243178]
  We propose a novel framework to adaptively learn the policy by jointly interacting with the environment and exploiting the expert demonstrations. Experimental results in various environments with multiple popular reinforcement learning algorithms show that the proposed approach can learn robustly with noisy demonstrations.
  arXiv  Detail & Related papers  (2020-06-14T06:03:06Z)
• State-Only Imitation Learning for Dexterous Manipulation [63.03621861920732]
  In this paper, we explore state-only imitation learning. We train an inverse dynamics model and use it to predict actions for state-only demonstrations. Our method performs on par with state-action approaches and considerably outperforms RL alone.
  arXiv  Detail & Related papers  (2020-04-07T17:57:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.