Evaluating the Effectiveness of Corrective Demonstrations and a Low-Cost Sensor for Dexterous Manipulation

• URL: http://arxiv.org/abs/2204.07631v1
• Date: Fri, 15 Apr 2022 19:55:46 GMT
• Title: Evaluating the Effectiveness of Corrective Demonstrations and a Low-Cost Sensor for Dexterous Manipulation
• Authors: Abhineet Jain, Jack Kolb, J.M. Abbess IV, Harish Ravichandar
• Abstract summary: Imitation learning is a promising approach to help robots acquire dexterous manipulation capabilities. We investigate characteristics of such additional demonstrations and their impact on performance. We show that inexpensive vision-based sensors, such as LeapMotion, can be used to dramatically reduce the cost of providing demonstrations.
• Score: 0.5669790037378094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation learning is a promising approach to help robots acquire dexterous manipulation capabilities without the need for a carefully-designed reward or a significant computational effort. However, existing imitation learning approaches require sophisticated data collection infrastructure and struggle to generalize beyond the training distribution. One way to address this limitation is to gather additional data that better represents the full operating conditions. In this work, we investigate characteristics of such additional demonstrations and their impact on performance. Specifically, we study the effects of corrective and randomly-sampled additional demonstrations on learning a policy that guides a five-fingered robot hand through a pick-and-place task. Our results suggest that corrective demonstrations considerably outperform randomly-sampled demonstrations, when the proportion of additional demonstrations sampled from the full task distribution is larger than the number of original demonstrations sampled from a restrictive training distribution. Conversely, when the number of original demonstrations are higher than that of additional demonstrations, we find no significant differences between corrective and randomly-sampled additional demonstrations. These results provide insights into the inherent trade-off between the effort required to collect corrective demonstrations and their relative benefits over randomly-sampled demonstrations. Additionally, we show that inexpensive vision-based sensors, such as LeapMotion, can be used to dramatically reduce the cost of providing demonstrations for dexterous manipulation tasks. Our code is available at https://github.com/GT-STAR-Lab/corrective-demos-dexterous-manipulation.

Related papers

• Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations [60.241144377865716]
  We consider the imitation of sub-optimal demonstrations, with both a small clean demonstration set and a large noisy set. We propose method by evaluating and imitating at the sub-demonstration level, encoding action primitives of varying quality into different skills.
  arXiv  Detail & Related papers  (2023-06-13T17:24:37Z)
• 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)
• Robustness of Demonstration-based Learning Under Limited Data Scenario [54.912936555876826]
  Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Why such demonstrations are beneficial for the learning process remains unclear since there is no explicit alignment between the demonstrations and the predictions. In this paper, we design pathological demonstrations by gradually removing intuitively useful information from the standard ones to take a deep dive of the robustness of demonstration-based sequence labeling.
  arXiv  Detail & Related papers  (2022-10-19T16:15:04Z)
• Extraneousness-Aware Imitation Learning [25.60384350984274]
  Extraneousness-Aware Learning (EIL) learns visuomotor policies from third-person demonstrations with extraneous subsequences. EIL learns action-conditioned observation embeddings in a self-supervised manner and retrieves task-relevant observations across visual demonstrations. Experimental results show that EIL outperforms strong baselines and achieves comparable policies to those trained with perfect demonstration.
  arXiv  Detail & Related papers  (2022-10-04T04:42:26Z)
• 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)
• 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)
• Robust Maximum Entropy Behavior Cloning [15.713997170792842]
  Imitation learning (IL) algorithms use expert demonstrations to learn a specific task. Most of the existing approaches assume that all expert demonstrations are reliable and trustworthy, but what if there exist some adversarial demonstrations among the given data-set? We propose a novel general frame-work to directly generate a policy from demonstrations that autonomously detect the adversarial demonstrations and exclude them from the data set.
  arXiv  Detail & Related papers  (2021-01-04T22:08:46Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)
• 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)
• Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations [78.94386823185724]
  Imitation learning learns effectively in sparse-rewarded tasks by leveraging the existing expert demonstrations. In practice, collecting a sufficient amount of expert demonstrations can be prohibitively expensive. We propose Self-Adaptive Learning (SAIL) that can achieve (near) optimal performance given only a limited number of sub-optimal demonstrations.
  arXiv  Detail & Related papers  (2020-04-01T15:57:15Z)

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.