Deep Adversarial Reinforcement Learning for Object Disentangling

• URL: http://arxiv.org/abs/2003.03779v2
• Date: Wed, 17 Mar 2021 14:30:17 GMT
• Title: Deep Adversarial Reinforcement Learning for Object Disentangling
• Authors: Melvin Laux, Oleg Arenz, Jan Peters, Joni Pajarinen
• Abstract summary: We present a novel adversarial reinforcement learning (ARL) framework for disentangling waste objects. The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states. We show that our method can generalize from training to test scenarios by training an end-to-end system for robot control to solve a challenging object disentangling task.
• Score: 36.66974848126079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful robotic RL applications such as in-hand manipulation. However, most robotic RL relies on a well known initial state distribution. In real-world tasks, this information is however often not available. For example, when disentangling waste objects the actual position of the robot w.r.t.\ the objects may not match the positions the RL policy was trained for. To solve this problem, we present a novel adversarial reinforcement learning (ARL) framework. The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states. We train the protagonist and the adversary jointly to allow them to adapt to the changing policy of their opponent. We show that our method can generalize from training to test scenarios by training an end-to-end system for robot control to solve a challenging object disentangling task. Experiments with a KUKA LBR+ 7-DOF robot arm show that our approach outperforms the baseline method in disentangling when starting from different initial states than provided during training.

Related papers

• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation [61.7171775202833]
  We introduce an efficient system for learning dexterous manipulation skills withReinforcement learning. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. Our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy.
  arXiv  Detail & Related papers  (2023-09-06T19:05:31Z)
• Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration [8.343657309038285]
  Reinforcement Learning is a powerful framework for developing such robot controllers. We propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers.
  arXiv  Detail & Related papers  (2023-08-04T16:55:00Z)
• Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data [101.43350024175157]
  Self-supervised learning has the potential to decrease the amount of human annotation and engineering effort required to learn control strategies. Our work builds on prior work showing that the reinforcement learning (RL) itself can be cast as a self-supervised problem. We demonstrate that a self-supervised RL algorithm based on contrastive learning can solve real-world, image-based robotic manipulation tasks.
  arXiv  Detail & Related papers  (2023-06-06T01:36:56Z)
• Reinforcement Learning for UAV control with Policy and Reward Shaping [0.7127008801193563]
  This study teaches an RL agent to control a drone using reward-shaping and policy-shaping techniques simultaneously. The results show that an agent trained simultaneously with both techniques obtains a lower reward than an agent trained using only a policy-based approach.
  arXiv  Detail & Related papers  (2022-12-06T14:46:13Z)
• Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning [70.70104870417784]
  Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. In practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. In this work, we study how these challenges can be tackled by effective utilization of diverse offline datasets collected from previously seen tasks.
  arXiv  Detail & Related papers  (2022-07-11T08:31:22Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks [70.56451186797436]
  We study how to use meta-reinforcement learning to solve the bulk of the problem in simulation. We demonstrate our approach by training an agent to successfully perform challenging real-world insertion tasks.
  arXiv  Detail & Related papers  (2020-04-29T18:00:22Z)

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.