We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving an integer program. Our method generalizes across system dynamics and learns a guaranteed subset of the constraint. We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations. We demonstrate our method on linear and nonlinear system dynamics, show that it can be modified to work with suboptimal demonstrations, and that it can also be used to learn constraints in a feature space.
Learning from Demonstration with Failure Awareness for Safe Robot Navigation
Department of Mechanical Engineering, The Hong Kong Polytechnic University（香港理工大学机械工程系）；College of Information Science and Technology, Eastern Institute of Technology（东华理工大学信息科学学院）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（香港理工大学航空与航空工程系）
Active Constraint Learning in High Dimensions from Demonstrations
School of Mechanical Engineering Georgia Institute of Technology（机械工程学院佐治亚理工学院）；School of Electrical and Computer Engineering Georgia Institute of Technology（电气与计算机工程学院佐治亚理工学院）；Schools of Cybersecurity & Privacy and Aerospace Engineering Georgia Institute of Technology（网络安全与隐私学院及航空航天工程学院佐治亚理工学院）
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations. To reduce the ill-posedness of the constraint recovery problem, our method uses hit-and-run sampling to generate lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a representation of the unsafe set that is compatible with the data by solving an integer program in that representation's parameter space. Our method can either leverage a known parameterization or incrementally grow a parameterization while remaining consistent with th…
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without this knowledge, a robot may infer incorrect reward functions that lead to undesirable or unsafe control policies. Recent work has proposed an LfD framework where a user provides a set of formal task specifications to guide LfD, to address the challenge of reward shaping. However, in this framework, specifications are manually ordered in a performance graph (a partial order that specifies relative importance between t…
Imperial College London（帝国理工学院）；Dyson School of Design Engineering（戴森设计工程学院）
Achieving successful robotic manipulation is an essential step towards robots being widely used in industry and home settings. Recently, many learning-based methods have been proposed to tackle this challenge, with imitation learning showing great promise. However, imperfect demonstrations and a lack of feedback from teleoperation systems may lead to poor or even unsafe results. In this work we explore the effect of demonstrator force feedback on imitation learning, using a feedback glove and a robot arm to render fingertip-level and palm-level forces, respectively. 10 participants recorded 5 …
Technische Universität Berlin（柏林工业大学）；Research Cluster of Excellence ”Science of Intelligence”（“智能科学”卓越研究集群）
We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms. The extracted policy from a single human demonstration generalizes to different mechanisms of the same type and is robust against environmental variations. The key to achieving such generalization and robustness from a single human demonstration is to autonomously augment the initial demonstration to gather additional information through purposefully interacting with the environment. Our real-world experiments on complex mechanisms with multi-DOF demonstrate that our appro…
Many methods in learning from demonstration assume that the demonstrator has knowledge of the full environment. However, in many scenarios, a demonstrator only sees part of the environment and they continuously replan as they gather information. To plan new paths or to reconstruct the environment, we must consider the visibility constraints and replanning process of the demonstrator, which, to our knowledge, has not been done in previous work. We consider the problem of inferring obstacle configurations in a 2D environment from demonstrated paths for a point robot that is capable of seeing in …
Learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures. Adding explicit constraints to avoid unsafe states is generally not possible when the state representations are complex. Furthermore, enforcing these constraints during execution of the learned policy can be challenging in environments where dynamics are difficult to model such as push mechanics in grasping. In this paper, we propose Derivative-Free Recovery (DFR), a two-phase method for generating robust policies from demonstrations…
Tulane University（杜兰大学）；European University Institute（欧洲大学学院）；University of West Florida（西佛罗里达大学）；IBM Research（IBM研究院）；Institute for Human and Machine Cognition（人类与机器认知研究所）
Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective norms and our own personal objectives. To create effective AI-human teams, we must equip AI agents with a model of how humans make trade-offs in complex, constrained environments. These agents will be able to mirror human behavior or to draw human attention to situations where decision making could be improved. To this end, we propose a novel inverse reinforcement l…
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统认知与决策智能国家重点实验室）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；PKU-PsiBot Joint Lab（北大-鹏城实验室联合实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Teaching contact-rich tasks from visual demonstrations by constraint extraction
Fraunhofer IPK（弗劳恩霍夫生产设备与设计技术研究所）；Politecnico di Milano（米兰理工大学）；Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达莱莫勒人工智能研究所（IDSIA））；Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)（瑞士意大利语区应用科学大学（SUPSI））；Università della Svizzera Italiana (USI)（瑞士意大利语区大学（USI））
From a Single Demonstration to a General Policy for Contact-Rich Manipulation
Robotics and Biology Laboratory, Technische Universität Berlin（技术大学柏林机器人与生物学实验室）；Science of Intelligence, Research Cluster of Excellence, Berlin（智能科学，卓越研究集群，柏林）；Robotics Institute Germany（德国机器人研究所）
University of Toronto Robotics Institute（多伦多大学机器人研究所）；Vector Institute（向量研究所）；Montreal Institute of Learning Algorithms (MILA)（蒙特利尔学习算法研究所）；McGill University（麦吉尔大学）
The potential benefits of model-free reinforcement learning to real robotics systems are limited by its uninformed exploration that leads to slow convergence, lack of data-efficiency, and unnecessary interactions with the environment. To address these drawbacks we propose a method that combines reinforcement and imitation learning by shaping the reward function with a state-and-action-dependent potential that is trained from demonstration data, using a generative model. We show that this accelerates policy learning by specifying high-value areas of the state and action space that are worth exp…
Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany（慕尼黑机器人与机器智能研究所（MIRMI），技术大学慕尼黑（TUM））；Robert Bosch Center for Cyber-Physical Systems (RBCCPS), Indian Institute of Science (IISc), Bangalore, India（罗伯特·博世网络物理系统中心（RBCCPS），印度科学研究院（IISc））；Automation and Robotics Laboratory, Universidade de Brasília（自动化与机器人实验室，巴西布拉斯伊利亚大学）；Indian Institute of Technology (IIT), Kharagpur, India（印度理工学院（IIT）克达尔格普分校）；Mohamed Bin Zayed University of Artificial Intelligence (MBUZAI), Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学（MBUZAI），阿布扎赫德，阿联酋）
University of Rochester（罗切斯特大学）；University of California San Diego（加州大学圣地亚哥分校）
Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert…
Robotics at Google（谷歌机器人部门）；X, The Moonshot Factory（X（登月工厂））；UC Berkeley（加州大学伯克利分校）
Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experience.Both methods have complementarystrengths and weaknesses: RL can reach a high level of performance, but requiresexploration, which can be very time consuming and unsafe; IL does not requireexploration, but only learns skills that are as good as the provided demonstrations.Can a single method combine the strengths of both approaches? A number ofprior methods have aimed to address this question, proposing a variety…
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work on imperfect demonstrations, we first define an imperfect expert setting for RLfD in a formal way, and then point out that previous methods suffer from two issues in terms of optimality and convergence, respectively. Upon the theoretical findings we have derived, we tackle these t…
Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards
Seoul National University（首尔大学）；ASRI（先进技术与科学研究院（ASRI））；Interdisciplinary Program in Artificial Intelligence（人工智能跨学科项目）；Department of Electrical and Computer Engineering（电气与计算机工程系）
We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner. Our method uses the Karush-Kuhn-Tucker (KKT) optimality conditions of the demonstrations within a mixed integer linear program (MILP) to learn constraints which are consistent with the local optimality of the demonstrations, by either using a known constraint parameterization or by incrementally growing a parameterization that is consistent with the demonstrations. We provide theoretical guarantees on the conservativeness of the r…
This paper proposes a method to learn from human demonstration compliant contact motions, which take advantage of interaction forces between workpieces to align them, even when contact force may occur from different directions on different instances of reproduction. To manage the uncertainty in unstructured conditions, the motions learned with our method can be reproduced with an impedance controller. Learning from Demonstration is used because the planning of compliant motions in 3-D is computationally intractable. The proposed method will learn an individual compliant motion, many of which c…
Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所（IRIM））
Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clutter-free environments. This prevents the demonstrations from being affected by irrelevant objects, whose influence can obfuscate the true intention of the human or the constraints of the desired skill. However, it is unrealistic to assume that the robot's environment can always be restructured to remove clutter when capturing human demonstrations. To contend with this problem, we develop an importance weighted batch …
Constrained objects, such as doors and drawers are often complex and share a similar structure in the human environment. A robot needs to interact accurately with constrained objects to safely and successfully complete a task. Learning from Demonstration offers an appropriate path to learn the object structure of the demonstration for unknown objects for unknown tasks. There is work that extracts the kinematic model from motion. However, the gap remains when the robot faces a new object with a similar model but different contexts, e.g. size, appearance, etc. In this paper, we propose a framewo…
Institut de Robotica i Informatica Industrial (IRI)（工业机器人与信息技术研究所）；Human Centered Robotics Laboratory (HCRL)（以人为中心的机器人实验室）
Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks. Learning from demonstration is arising as a promising paradigm for transferring skills to robots. It allows to implicitly learn task constraints from observing the motion executed by a human teacher, which can enable adaptive behavior. We present a novel Gaussian-Process-based learning from demonstration approach. This probabilistic representation allows to generalize over multiple demonstrations, and encode variability along the different phases of the task. In this paper, we addres…
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
The University of Hong Kong（香港大学）；JD.com (JingDong)（京东）；Nanyang Technological University (NTU)（南洋理工大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；Huawei Technologies（华为技术有限公司）；The Chinese University of Hong Kong (CUHK)（香港中文大学）
Georgia Institute of Technology（佐治亚理工学院）；Ford Motor Company（福特汽车公司）
Barrier function-based inequality constraints are a means to enforce safety specifications for control systems. When used in conjunction with a convex optimization program, they provide a computationally efficient method to enforce safety for the general class of control-affine systems. One of the main assumptions when taking this approach is the a priori knowledge of the barrier function itself, i.e., knowledge of the safe set. In the context of navigation through unknown environments where the locally safe set evolves with time, such knowledge does not exist. This manuscript focuses on the s…
We present a method for learning to satisfy uncertain constraints from demonstrations. Our method uses robust optimization to obtain a belief over the potentially infinite set of possible constraints consistent with the demonstrations, and then uses this belief to plan trajectories that trade off performance with satisfying the possible constraints. We use these trajectories in a closed-loop policy that executes and replans using belief updates, which incorporate data gathered during execution. We derive guarantees on the accuracy of our constraint belief and probabilistic guarantees on plan s…
We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations. Our approach uses the Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on the demonstrations the constraint is tight, and a scaling of the constraint gradient at those states. We then train a GP representation of the constraint which is consistent with and which generalizes this information. We further show that the GP uncertainty can be used within a kinodynamic RRT to plan probabilistically-safe trajectories, and that we can exploit the GP structure withi…
Neural networks have recently become popular for a wide variety of uses, but have seen limited application in safety-critical domains such as robotics near and around humans. This is because it remains an open challenge to train a neural network to obey safety constraints. Most existing safety-related methods only seek to verify that already-trained networks obey constraints, requiring alternating training and verification. Instead, this work proposes a constrained method to simultaneously train and verify a feedforward neural network with rectified linear unit (ReLU) nonlinearities. Constrain…
Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；AIRI（AIRI（人工智能研究所））；Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers
Electrical Engineering and Computer Science, University of California, Irvine（加州大学欧文分校电气工程与计算机科学系）；Electrical and Computer Engineering, University of Virginia（弗吉尼亚大学电气与计算机工程系）；Mechanical Engineering, Iowa State University（爱荷华州立大学机械工程系）
In this work, we consider the problem of learning a feed-forward neural network controller to safely steer an arbitrarily shaped planar robot in a compact and obstacle-occluded workspace. Unlike existing methods that depend strongly on the density of data points close to the boundary of the safe state space to train neural network controllers with closed-loop safety guarantees, here we propose an alternative approach that lifts such strong assumptions on the data that are hard to satisfy in practice and instead allows for graceful safety violations, i.e., of a bounded magnitude that can be spa…
Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
Indian Institute of Technology Kharagpur（印度理工学院科拉格浦分校）；Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（慕尼黑机器人与智能机械研究所，慕尼黑技术大学）；University of Brasilia（巴西大学）；Carnegie Mellon University（卡内基梅隆大学）；Mechanical Engineering Program at New York University Abu Dhabi（纽约大学阿布扎克分校机械工程项目）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
University of Edinburgh（爱丁堡大学）；Monash University（莫纳什大学）
Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of non-holonomic constraints. We obtain the closed-form expression f…
We propose a new scheme to learn motion planning constraints from human driving trajectories. Behavioral and motion planning are the key components in an autonomous driving system. The behavioral planning is responsible for high-level decision making required to follow traffic rules and interact with other road participants. The motion planner role is to generate feasible, safe trajectories for a self-driving vehicle to follow. The trajectories are generated through an optimization scheme to optimize a cost function based on metrics related to smoothness, movability, and comfort, and subject t…
In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active learning, have been successful in applications, on real-world systems they must provably avoid unsafe decisions. To this end, safe IML algorithms must carefully learn about a priori unknown constraints without making unsafe decisions. Existing algorithms for this problem learn about the safety of all decisions to ensure convergence. This is sample-inefficient, as it explores decisions that are not relevant for the o…
University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）
Maximum likelihood constraint inference is a powerful technique for identifying unmodeled constraints that affect the behavior of a demonstrator acting under a known objective function. However, it was originally formulated only for discrete state-action spaces. Continuous dynamics are more useful for modeling many real-world systems of interest, including the movements of humans and robots. We present a method to generate a tabular state-action space that approximates continuous dynamics and can be used for constraint inference on demonstrations that obey the true system dynamics. We then dem…
