Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

• URL: http://arxiv.org/abs/2004.00716v1
• Date: Wed, 1 Apr 2020 21:50:11 GMT
• Title: Constrained-Space Optimization and Reinforcement Learning for Complex Tasks
• Authors: Ya-Yen Tsai, Bo Xiao, Edward Johns, Guang-Zhong Yang
• Abstract summary: Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks.
• Score: 42.648636742651185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.

Related papers

• SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation [58.14969377419633]
  We propose spire, a system that decomposes tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We find that spire outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance.
  arXiv  Detail & Related papers  (2024-10-23T17:42:07Z)
• Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning [6.683222869973898]
  This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers. By employing reinforcement learning, we train an agent to acquire the necessary skills for proficient manipulation. Two knowledge transfer strategies, fine-tuning and curriculum learning, were utilized within the soft actor-critic architecture.
  arXiv  Detail & Related papers  (2024-03-25T23:19:19Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition [10.072992621244042]
  We propose a novel method for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (Tactile-AIRL) To enhance the performance of reinforcement learning (RL), we introduce active inference, which integrates model-based techniques and intrinsic curiosity into the RL process. We demonstrate that our method achieves significantly high training efficiency in non-prehensile objects pushing tasks.
  arXiv  Detail & Related papers  (2023-11-19T10:19:22Z)
• Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation [1.7901837062462316]
  This paper aims to define and incorporate the natural symmetry present in physical robotic environments. The proposed method is validated via two point-to-point reaching tasks of an industrial arm, with and without an obstacle. A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications.
  arXiv  Detail & Related papers  (2023-04-12T11:38:01Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning [7.51557557629519]
  We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of, in addition to a main task, multiple auxiliary tasks. This affords many benefits: learning efficiency is improved for main tasks with challenging bottleneck transitions, expert data becomes reusable between tasks, and transfer learning through the reuse of learned auxiliary task models becomes possible.
  arXiv  Detail & Related papers  (2021-12-16T14:58:08Z)
• Human-in-the-Loop Imitation Learning using Remote Teleoperation [72.2847988686463]
  We build a data collection system tailored to 6-DoF manipulation settings. We develop an algorithm to train the policy iteratively on new data collected by the system. We demonstrate that agents trained on data collected by our intervention-based system and algorithm outperform agents trained on an equivalent number of samples collected by non-interventional demonstrators.
  arXiv  Detail & Related papers  (2020-12-12T05:30:35Z)
• An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards [14.937474939057596]
  It is important for robotic manipulators to learn to accomplish tasks even if they are only provided with very sparse instruction signals. This paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2020-10-15T19:06:21Z)

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.