CAIMAN: Causal Action Influence Detection for Sample Efficient Loco-manipulation

• URL: http://arxiv.org/abs/2502.00835v1
• Date: Sun, 02 Feb 2025 16:16:53 GMT
• Title: CAIMAN: Causal Action Influence Detection for Sample Efficient Loco-manipulation
• Authors: Yuanchen Yuan, Jin Cheng, Núria Armengol Urpí, Stelian Coros,
• Abstract summary: We present CAIMAN, a novel framework for learning loco-manipulation that relies solely on sparse task rewards. We employ a hierarchical control strategy, combining a low-level locomotion policy with a high-level policy that prioritizes task-relevant velocity commands. We demonstrate the framework's superior sample efficiency, adaptability to diverse environments, and successful transfer to hardware without fine-tuning.
• Score: 17.94272840532448
• License:
• Abstract: Enabling legged robots to perform non-prehensile loco-manipulation with large and heavy objects is crucial for enhancing their versatility. However, this is a challenging task, often requiring sophisticated planning strategies or extensive task-specific reward shaping, especially in unstructured scenarios with obstacles. In this work, we present CAIMAN, a novel framework for learning loco-manipulation that relies solely on sparse task rewards. We leverage causal action influence to detect states where the robot is in control over other entities in the environment, and use this measure as an intrinsically motivated objective to enable sample-efficient learning. We employ a hierarchical control strategy, combining a low-level locomotion policy with a high-level policy that prioritizes task-relevant velocity commands. Through simulated and real-world experiments, including object manipulation with obstacles, we demonstrate the framework's superior sample efficiency, adaptability to diverse environments, and successful transfer to hardware without fine-tuning. The proposed approach paves the way for scalable, robust, and autonomous loco-manipulation in real-world applications.

Related papers

• COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping [56.907940167333656]
  Occluded robot grasping is where the desired grasp poses are kinematically infeasible due to environmental constraints such as surface collisions. Traditional robot manipulation approaches struggle with the complexity of non-prehensile or bimanual strategies commonly used by humans. We introduce Constraint-based Manipulation for Bimanual Occluded Grasping (COMBO-Grasp), a learning-based approach which leverages two coordinated policies.
  arXiv  Detail & Related papers  (2025-02-12T01:31:01Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  We introduce a novel framework for learning world models. By providing a scalable and robust framework, we pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• 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)
• Latent Exploration for Reinforcement Learning [87.42776741119653]
  In Reinforcement Learning, agents learn policies by exploring and interacting with the environment. We propose LATent TIme-Correlated Exploration (Lattice), a method to inject temporally-correlated noise into the latent state of the policy network.
  arXiv  Detail & Related papers  (2023-05-31T17:40:43Z)
• Efficient Learning of High Level Plans from Play [57.29562823883257]
  We present Efficient Learning of High-Level Plans from Play (ELF-P), a framework for robotic learning that bridges motion planning and deep RL. We demonstrate that ELF-P has significantly better sample efficiency than relevant baselines over multiple realistic manipulation tasks.
  arXiv  Detail & Related papers  (2023-03-16T20:09:47Z)
• Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments [18.348489257164356]
  We propose a system for efficient skill acquisition that leverages an object-centric generative model (OCGM) for versatile goal identification. OCGM enables one-shot target object identification and re-identification in new scenes, allowing MP to guide the robot to the target object while avoiding obstacles.
  arXiv  Detail & Related papers  (2023-03-06T18:49:59Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower [30.032847855193864]
  blowing controller must continually adapt to unexpected changes from its actions. We introduce a multi-frequency version of the spatial action maps framework. This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control.
  arXiv  Detail & Related papers  (2022-04-05T17:55:58Z)
• Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space [7.116986445066885]
  Reinforcement Learning has led to promising results on a range of challenging decision-making tasks. Fast human-like adaptive control methods can optimize complex robotic interactions, yet fail to integrate multimodal feedback needed for unstructured tasks. We propose to factor the learning problem in a hierarchical learning and adaption architecture to get the best of both worlds.
  arXiv  Detail & Related papers  (2021-10-19T12:09:02Z)
• Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping [17.2022039806473]
  A multi-task reinforcement learning-based mobile manipulation control framework is proposed to achieve general dynamic object tracking and grasping. Experiments show that our policy trained can adapt to unseen random dynamic trajectories with about 0.1m tracking error and 75% grasping success rate.
  arXiv  Detail & Related papers  (2020-06-07T21:18:36Z)
• Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives [89.34229413345541]
  We propose a conditioning scheme which avoids pitfalls by learning the controller and its conditioning in an end-to-end manner. Our model predicts complex action sequences based directly on a dynamic image representation of the robot motion. We report significant improvements in task success over representative MPC and IL baselines.
  arXiv  Detail & Related papers  (2020-03-19T15:04:37Z)

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.