Visual IRL for Human-Like Robotic Manipulation

• URL: http://arxiv.org/abs/2412.11360v1
• Date: Mon, 16 Dec 2024 01:23:13 GMT
• Title: Visual IRL for Human-Like Robotic Manipulation
• Authors: Ehsan Asali, Prashant Doshi,
• Abstract summary: We present a novel method for collaborative robots (cobots) to learn manipulation tasks and perform them in a human-like manner.<n>Our method falls under the learn-from-observation (LfO) paradigm, where robots learn to perform tasks by observing human actions.<n>We evaluate the performance of this approach on two different realistic manipulation tasks.
• Score: 5.167226775583172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel method for collaborative robots (cobots) to learn manipulation tasks and perform them in a human-like manner. Our method falls under the learn-from-observation (LfO) paradigm, where robots learn to perform tasks by observing human actions, which facilitates quicker integration into industrial settings compared to programming from scratch. We introduce Visual IRL that uses the RGB-D keypoints in each frame of the observed human task performance directly as state features, which are input to inverse reinforcement learning (IRL). The inversely learned reward function, which maps keypoints to reward values, is transferred from the human to the cobot using a novel neuro-symbolic dynamics model, which maps human kinematics to the cobot arm. This model allows similar end-effector positioning while minimizing joint adjustments, aiming to preserve the natural dynamics of human motion in robotic manipulation. In contrast with previous techniques that focus on end-effector placement only, our method maps multiple joint angles of the human arm to the corresponding cobot joints. Moreover, it uses an inverse kinematics model to then minimally adjust the joint angles, for accurate end-effector positioning. We evaluate the performance of this approach on two different realistic manipulation tasks. The first task is produce processing, which involves picking, inspecting, and placing onions based on whether they are blemished. The second task is liquid pouring, where the robot picks up bottles, pours the contents into designated containers, and disposes of the empty bottles. Our results demonstrate advances in human-like robotic manipulation, leading to more human-robot compatibility in manufacturing applications.

Related papers

• VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation [53.63540587160549]
  VidBot is a framework enabling zero-shot robotic manipulation using learned 3D affordance from in-the-wild monocular RGB-only human videos. VidBot paves the way for leveraging everyday human videos to make robot learning more scalable.
  arXiv  Detail & Related papers  (2025-03-10T10:04:58Z)
• DIRIGENt: End-To-End Robotic Imitation of Human Demonstrations Based on a Diffusion Model [16.26334759935617]
  We introduce DIRIGENt, a novel end-to-end diffusion approach to generate joint values from observing human demonstrations. We create a dataset in which humans imitate a robot and then use this collected data to train a diffusion model that enables a robot to imitate humans.
  arXiv  Detail & Related papers  (2025-01-28T09:05:03Z)
• Learning to Transfer Human Hand Skills for Robot Manipulations [12.797862020095856]
  We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Our approach learns a joint motion manifold that maps human hand movements, robot hand actions, and object movements in 3D, enabling us to infer one motion from others.
  arXiv  Detail & Related papers  (2025-01-07T22:33:47Z)
• Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers [36.497624484863785]
  We introduce Vid2Robot, an end-to-end video-conditioned policy that takes human videos demonstrating manipulation tasks as input and produces robot actions. Our model is trained with a large dataset of prompt video-robot trajectory pairs to learn unified representations of human and robot actions from videos. We evaluate Vid2Robot on real-world robots and observe over 20% improvement over BC-Z when using human prompt videos.
  arXiv  Detail & Related papers  (2024-03-19T17:47:37Z)
• What Matters to You? Towards Visual Representation Alignment for Robot Learning [81.30964736676103]
  When operating in service of people, robots need to optimize rewards aligned with end-user preferences. We propose Representation-Aligned Preference-based Learning (RAPL), a method for solving the visual representation alignment problem.
  arXiv  Detail & Related papers  (2023-10-11T23:04:07Z)
• ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
  This paper introduces a novel deep-learning approach for human-to-robot motion. Our method does not require paired human-to-robot data, which facilitates its translation to new robots. Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
  arXiv  Detail & Related papers  (2023-09-11T08:55:04Z)
• Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Video Demonstrations [66.47064743686953]
  Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation. Videos of humans performing tasks, on the other hand, are much cheaper to collect since they eliminate the need for expertise in robotic teleoperation. In this work, we augment narrow robotic imitation datasets with broad unlabeled human video demonstrations to greatly enhance the generalization of eye-in-hand visuomotor policies.
  arXiv  Detail & Related papers  (2023-07-12T07:04:53Z)
• Zero-Shot Robot Manipulation from Passive Human Videos [59.193076151832145]
  We develop a framework for extracting agent-agnostic action representations from human videos. Our framework is based on predicting plausible human hand trajectories. We deploy the trained model zero-shot for physical robot manipulation tasks.
  arXiv  Detail & Related papers  (2023-02-03T21:39:52Z)
• Learning Reward Functions for Robotic Manipulation by Observing Humans [92.30657414416527]
  We use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies. The learned rewards are based on distances to a goal in an embedding space learned using a time-contrastive objective.
  arXiv  Detail & Related papers  (2022-11-16T16:26:48Z)
• Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots [129.46920552019247]
  We propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.
  arXiv  Detail & Related papers  (2021-02-09T10:34:32Z)

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.