Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks

• URL: http://arxiv.org/abs/2008.10471v1
• Date: Mon, 24 Aug 2020 14:20:05 GMT
• Title: Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
• Authors: Leonel Rozo, Meng Guo, Andras G. Kupcsik, Marco Todescato, Philipp Schillinger, Markus Giftthaler, Matthias Ochs, Markus Spies, Nicolai Waniek, Patrick Kesper, Mathias B\"uerger
• Abstract summary: We propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks.
• Score: 16.308562047398542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations. In this work, we propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. This approach significantly reduces manual modeling efforts, while ensuring a high degree of flexibility and re-usability of learned skills. Given a task goal and a set of generic skills, our framework computes smooth transitions between skill instances. To compute the corresponding optimal end-effector trajectory in task space we rely on Riemannian optimal controller. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks.

Related papers

• Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation [17.222197596599685]
  We propose a Skill Learning approach that discovers composable behaviors by solving a large number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform.
  arXiv  Detail & Related papers  (2024-10-07T09:19:13Z)
• Practice Makes Perfect: Planning to Learn Skill Parameter Policies [34.51008914846429]
  In this work, we focus on the active learning problem of choosing which skills to practice to maximize expected future task success. We propose that the robot should estimate the competence of each skill, extrapolate the competence, and situate the skill in the task distribution through competence-aware planning. This approach is implemented within a fully autonomous system where the robot repeatedly plans, practices, and learns without any environment resets.
  arXiv  Detail & Related papers  (2024-02-22T23:58:26Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Learning and Adapting Agile Locomotion Skills by Transferring Experience [71.8926510772552]
  We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks. We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments.
  arXiv  Detail & Related papers  (2023-04-19T17:37:54Z)
• Divide & Conquer Imitation Learning [75.31752559017978]
  Imitation Learning can be a powerful approach to bootstrap the learning process. We present a novel algorithm designed to imitate complex robotic tasks from the states of an expert trajectory. We show that our method imitates a non-holonomic navigation task and scales to a complex simulated robotic manipulation task with very high sample efficiency.
  arXiv  Detail & Related papers  (2022-04-15T09:56:50Z)
• Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models [29.34375999491465]
  A core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills to long-horizon tasks, robots should be able to learn and later refine their skills in a structured manner. We proposeSAC-GMM, a novel hybrid approach that learns robot skills through a dynamical system and adapts the learned skills in their own trajectory distribution space.
  arXiv  Detail & Related papers  (2021-11-25T15:36:11Z)
• Discovering Generalizable Skills via Automated Generation of Diverse Tasks [82.16392072211337]
  We propose a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills, our method pairs each skill with a unique task produced by a trainable task generator. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.
  arXiv  Detail & Related papers  (2021-06-26T03:41:51Z)
• SKID RAW: Skill Discovery from Raw Trajectories [23.871402375721285]
  It is desirable to only demonstrate full task executions instead of all individual skills. We propose a novel approach that simultaneously learns to segment trajectories into reoccurring patterns. The approach learns a skill conditioning that can be used to understand possible sequences of skills.
  arXiv  Detail & Related papers  (2021-03-26T17:27:13Z)
• Learning compositional models of robot skills for task and motion planning [39.36562555272779]
  We learn to use sensorimotor primitives to solve complex long-horizon manipulation problems. We use state-of-the-art methods for active learning and sampling. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions.
  arXiv  Detail & Related papers  (2020-06-08T20:45:34Z)
• Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning [81.12201426668894]
  We develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks. We show that our proposed algorithm provides substantial improvement in learning efficiency, making reward-free real-world training feasible. We also demonstrate that the learned skills can be composed using model predictive control for goal-oriented navigation, without any additional training.
  arXiv  Detail & Related papers  (2020-04-27T17:38:53Z)
• Learning Agile Robotic Locomotion Skills by Imitating Animals [72.36395376558984]
  Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. We present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
  arXiv  Detail & Related papers  (2020-04-02T02:56:16Z)

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.