Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot's physical limits. These limits are especially relevant in dynamic manipulation where high velocities and accelerations are typical. To address this problem, we propose a framework that first maps a dynamic demonstration into a motion that respects the robot's constraints using a constrained Dynamic Movement Primitive. Second, the resulting object state is further optimized by quasi-static refinement motions to optimize task performance metrics. This allows both efficiently altering the object state by dynamic motions and stable small-scale refinements. We evaluate the framework in the challenging task of bag opening, designing the system BILBO: Bimanual dynamic manipulation using Imitation Learning for Bag Opening. Our results show that BILBO can successfully open a wide range of crumpled bags, using a demonstration with a single bag. See supplementary material at https://sites.google.com/view/bilbo-bag.
ViTac-Tracing: Visual-Tactile Imitation Learning of Deformable Object Tracing
Robot Perception Laboratory, Department of Engineering, King’s College London（机器人感知实验室，工程系，伦敦国王学院）；Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London（个人机器人实验室，电子与电气工程系，伦敦帝国学院）
Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation
Department of Robotics, Tohoku University（东大理学院机器人系）；JC STEM Lab of Robotics for Soft Materials, the Department of Electrical and Computer Engineering, Faculty of Engineering, The University of Hong Kong（香港大学工程学院电气与计算机工程系软材料机器人实验室）
Robot control for tactile feedback-based manipulation can be difficult due to the modeling of physical contacts, partial observability of the environment, and noise in perception and control. This work focuses on solving partial observability of contact-rich manipulation tasks as a Sequence-to-Sequence (Seq2Seq)} Imitation Learning (IL) problem. The proposed Seq2Seq model produces a robot-environment interaction sequence to estimate the partially observable environment state variables. Then, the observed interaction sequence is transformed to a control sequence for the task itself. The propose…
This study proposes an imitation learning method based on force and position information. Force information is required for precise object manipulation but is difficult to obtain because the acting and reaction forces cannnot be separated. To separate the forces, we proposed to introduce bilateral control, in which the acting and reaction forces are divided using two robots. In the proposed method, two models of neural networks learn a task; to draw a line along a ruler. We verify the possibility that force information is essential to imitate the human skill of object manipulation.
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sony AI（索尼人工智能）
We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop visuomotor policies for robot manipulation. Our approach constructs object-centric representations based on general object proposals from a pre-trained vision model. VIOLA uses a transformer-based policy to reason over these representations and attend to the task-relevant visual factors for action prediction. Such object-based structural priors improve deep imitation learning algorithm's robustness against object variations and environmental perturbations. We quantitatively evaluate VIOLA in simulation an…
Khoury College of Computer Sciences（Khoury计算机科学学院）；Northeastern University（东北大学）
In robotic manipulation, acquiring samples is extremely expensive because it often requires interacting with the real world. Traditional image-level data augmentation has shown the potential to improve sample efficiency in various machine learning tasks. However, image-level data augmentation is insufficient for an imitation learning agent to learn good manipulation policies in a reasonable amount of demonstrations. We propose Simulation-augmented Equivariant Imitation Learning (SEIL), a method that combines a novel data augmentation strategy of supplementing expert trajectories with simulated…
National University of Singapore（新加坡国立大学）；Sea AI Lab（Sea人工智能实验室）
Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning algorithms to train a policy with analytic gradients through environment dynamics, which significantly facilitates the development of DOM algorithms. However, existing DOM benchmarks are either single-object-based or non-differentiable. This leaves the questions of 1) how a task-specific algorithm performs on other tasks and 2) how a differentiable-physics-based algorithm compares with the non-differentiable ones in ge…
University of Maryland at College Park（马里兰大学帕克分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；City University of Hong Kong（香港城市大学）；the University of Hong Kong（香港大学）
We present a novel approach for robust manipulation of high-DOF deformable objects such as cloth. Our approach uses a random forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random forest-based controller is determined automatically based on the training data consisting visual features and optimal control actions. This enables us to integrate the overall process of training data classification and controller optimization into an imitation learning (IL) approach. Our approach enables le…
Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, LIRIS, UMR5205（Lyon中央理工学院、CNRS、Claude Bernard Lyon 1大学、INSA Lyon、Lyon大学、LIRIS、UMR5205）；Beihang University（北航大学）；Dalian University of Technology（大连理工大学）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））
RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（日本国家先进工业科学技术研究院人工智能研究中心）；CNRS-AIST JRL (Joint Robotics Laboratory), IRL（法国国家科学研究中心与日本AIST联合机器人实验室）；Institute for AI and Robotics, Future Robotics Organization, Waseda University（早稻田大学未来机器人组织人工智能与机器人研究所）；AI Robot Association (AIRoA)（人工智能机器人协会）
Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation
The University of Tokyo（东京大学）；Graduate School of Information Science and Technology（信息科学与技术研究生院）；Laboratory for Intelligent Systems and Informatics（智能系统与信息学实验室）
TACT: Humanoid Whole-body Contact Manipulation through Deep Imitation Learning with Tactile Modality
CNRS-AIST JRL (Joint Robotics Laboratory), IRL and National Institute of Advanced Industrial Science and Technology (AIST)（CNRS-AIST联合机器人实验室、IRL和国家先进工业科学与技术研究院）；Tokyo University of Science（东京科学大学）；University of Tsukuba（茨口大学）
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has the potential to learn policies with high-dimensional observations as input, e.g., images. By applying GAIL to a real robot, perhaps robot policies can be obtained for daily activities like washing, folding clothes, cooking, and cleaning. However, human demonstration data are often imperfect due to mistakes, which degrade the performance of the resulting policies. We address this issue by focusing on the following features: 1) many robotic tasks are …
Generalizable object manipulation skills are critical for intelligent and multi-functional robots to work in real-world complex scenes. Despite the recent progress in reinforcement learning, it is still very challenging to learn a generalizable manipulation policy that can handle a category of geometrically diverse articulated objects. In this work, we tackle this category-level object manipulation policy learning problem via imitation learning in a task-agnostic manner, where we assume no handcrafted dense rewards but only a terminal reward. Given this novel and challenging generalizable poli…
University of Tsukuba（筑波大学）；Saitama University（埼玉大学）
Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful action data obtained during autonomous opera…
With the field of rigid-body robotics having matured in the last fifty years, routing, planning, and manipulation of deformable objects have recently emerged as a more untouched research area in many fields ranging from surgical robotics to industrial assembly and construction. Routing approaches for deformable objects which rely on learned implicit spatial representations (e.g., Learning-from-Demonstration methods) make them vulnerable to changes in the environment and the specific setup. On the other hand, algorithms that entirely separate the spatial representation of the deformable object …
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actu…
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning to robot manipulation have been limited to reactive control based on the states at the current time step. However, future robots will also be required to solve tasks utilizing their memory obtained by experience in complicated environments (e.g., when the robot is asked to find a previously used object on a shelf). In such a situation, simple deep imitation learning may fail because of distractions caused by complic…
University of California, Berkeley（加州大学伯克利分校）；Embodied Intelligence；OpenAI；International Computer Science Institute (ICSI)（国际计算机科学研究所（ICSI））
Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks. We also describe how imitation learning can learn deep neural network policies (mapping from pixels to actions) that can acquire the demonstrated skills. Our experiments showcase the effectiveness of our approach for learning visuomotor ski…
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
School of Computing and Augmented Intelligence at Arizona State University（亚利桑那州立大学计算与增强智能学院）；TU Darmstadt（达姆施塔特工业大学）；Robotics Institute at Carnegie Mellon University（卡内基梅隆大学机器人学院）
We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration, without requiring any prior knowledge of the object being interacted with. Our method models imitation learning as a state estimation problem, with the state defined as the end-effector's pose at the point where object interaction begins, as observed from the demonstration. By then modelling a manipulation task as a coarse, approach trajectory followed by a fine, interaction trajectory, this state estimator can be trained in a self-superv…
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI Thrust）；The Hong Kong University of Science and Technology（香港科技大学）
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation. In this paper, we propose a new platform and pipeline DexMV (Dexterous Manipulation from Videos) for imitation learning. We design a platform with: (i) a simulation system for complex dexterous manipulation tasks with a multi-finger robot hand and (ii) a computer vision system to record large-scale demonstrations of a human hand conducting the same tasks. In our novel pipeline, we extract 3D hand and object pos…
DeformGS: Scene Flow in Highly Deformable Scenes for Deformable Object Manipulation
Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；National University of Singapore（新加坡国立大学）；Technical University of Munich（慕尼黑工业大学）；NVIDIA（英伟达）
We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand. We introduce a novel single-camera teleoperation system to collect the 3D demonstrations efficiently with only an iPad and a computer. One key contribution of our system is that we construct a customized robot hand for each user in the physical simulator, which is a manipulator resembling the same kinematics structure and shape of the operator's hand. This provides an intuitive interface and avoid unstable human-robot hand r…
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）
We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the environment online and using guidance from experts to explore high dimensional spaces effectively. We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations…
Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of everyday objects. The small, curved, and slippery tips of chopsticks pose a challenge for picking up small objects, making them a suitably complex test case. This paper leverages human demonstrations to develop an autonomous chopsticks-equipped robotic manipulator. Due to the lack of accurate models for fine manipulation, we explore model-free imitation learning, which traditionally suffers from the covariate shift phenomenon that causes poor generalization. We propose two approaches to reduce covariate sh…
