Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration

• URL: http://arxiv.org/abs/2308.02459v1
• Date: Fri, 4 Aug 2023 16:55:00 GMT
• Title: Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration
• Authors: Juan Del Aguila Ferrandis, Jo\~ao Moura, Sethu Vijayakumar
• Abstract summary: 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.
• Score: 8.343657309038285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing robot controllers capable of achieving dexterous nonprehensile manipulation, such as pushing an object on a table, is challenging. The underactuated and hybrid-dynamics nature of the problem, further complicated by the uncertainty resulting from the frictional interactions, requires sophisticated control behaviors. Reinforcement Learning (RL) is a powerful framework for developing such robot controllers. However, previous RL literature addressing the nonprehensile pushing task achieves low accuracy, non-smooth trajectories, and only simple motions, i.e. without rotation of the manipulated object. We conjecture that previously used unimodal exploration strategies fail to capture the inherent hybrid-dynamics of the task, arising from the different possible contact interaction modes between the robot and the object, such as sticking, sliding, and separation. In this work, we propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies for arbitrary starting and target object poses, i.e. positions and orientations, and with improved accuracy. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers. Furthermore, we validate the transferability of the learned policies, trained entirely in simulation, to a physical robot hardware using the KUKA iiwa robot arm. See our supplemental video: https://youtu.be/vTdva1mgrk4.

Related papers

• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• Learning Extrinsic Dexterity with Parameterized Manipulation Primitives [8.7221770019454]
  We learn a sequence of actions that utilize the environment to change the object's pose. Our approach can control the object's state through exploiting interactions between the object, the gripper, and the environment. We evaluate our approach on picking box-shaped objects of various weight, shape, and friction properties from a constrained table-top workspace.
  arXiv  Detail & Related papers  (2023-10-26T21:28:23Z)
• Learning Vision-based Pursuit-Evasion Robot Policies [54.52536214251999]
  We develop a fully-observable robot policy that generates supervision for a partially-observable one. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild.
  arXiv  Detail & Related papers  (2023-08-30T17:59:05Z)
• Causal Policy Gradient for Whole-Body Mobile Manipulation [39.3461626518495]
  We introduce Causal MoMa, a new reinforcement learning framework to train policies for typical MoMa tasks. We evaluate the performance of Causal MoMa on three types of simulated robots across different MoMa tasks.
  arXiv  Detail & Related papers  (2023-05-04T23:23:47Z)
• Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation [35.34044822433743]
  Recent works in robotic manipulation have shown potential for tackling a range of tasks. We conjecture that this is due to the high-dimensional action space for joint control. In this paper, we take an alternative approach and separate the task of learning 'what to do' from 'how to do it' The whole-body robotic kinematic control is optimized to execute the high-dimensional joint motion to reach the goals in the workspace.
  arXiv  Detail & Related papers  (2023-03-07T16:31:13Z)
• Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity [22.01389127145982]
  A simple gripper can solve more complex manipulation tasks if it can utilize the external environment. We develop a system based on reinforcement learning to address these limitations. It demonstrates dynamic and contact-rich motions with a simple gripper that generalizes across objects with various size, density, surface friction, and shape with a 78% success rate.
  arXiv  Detail & Related papers  (2022-11-02T22:09:24Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)
• COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing [87.7257446869134]
  General contact-rich manipulation problems are long-standing challenges in robotics. Deep reinforcement learning has shown great potential in solving robot manipulation tasks. We propose COCOI, a deep RL method that encodes a context embedding of dynamics properties online.
  arXiv  Detail & Related papers  (2020-11-23T08:20:21Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)

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.