In this work, we present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature. Our method learns to accomplish these tasks using multiple statically placed but uncalibrated RGB camera views without building an explicit 3D representation such as a pointcloud or voxel grid. This multi-camera approach achieves superior task performance on difficult stacking and insertion tasks compared to single-view baselines. Single view robotic agents struggle from occlusion and challenges in estimating relative poses between points of interest. While full 3D scene representations (voxels or pointclouds) are obtainable from registered output of multiple depth sensors, several challenges complicate operating off such explicit 3D representations. These challenges include imperfect camera calibration, poor depth maps due to object properties such as reflective surfaces, and slower inference speeds over 3D representations compared to 2D images. Our use of static but uncalibrated cameras does not require camera-robot or camera-camera calibration making the proposed approach easy to setup and our use of \textit{sensor dropout} during training makes it resilient to the loss of camera-views after deployment.
University of Michigan（密歇根大学）；Collaborative Safety Research Center at the Toyota Motor North America Research & Development（丰田汽车北美研发中心协作安全研究中心）
This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. Most previous monocular 3D vehicle detection algorithms focused on cameras on vehicles from the perspective of a driver, and assumed known intrinsic and extrinsic calibration. On the contrary, this paper focuses on the same task using uncalibrated monocular traffic cameras. We observe that the homography between the road plane and the image plane is essential to 3D vehicle detection and the data synthesis for this task, and the homography can be estimated without the camera i…
Learning to solve precision-based manipulation tasks from visual feedback using Reinforcement Learning (RL) could drastically reduce the engineering efforts required by traditional robot systems. However, performing fine-grained motor control from visual inputs alone is challenging, especially with a static third-person camera as often used in previous work. We propose a setting for robotic manipulation in which the agent receives visual feedback from both a third-person camera and an egocentric camera mounted on the robot's wrist. While the third-person camera is static, the egocentric camera…
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…
As cameras and inertial sensors are becoming ubiquitous in mobile devices and robots, it holds great potential to design visual-inertial navigation systems (VINS) for efficient versatile 3D motion tracking which utilize any (multiple) available cameras and inertial measurement units (IMUs) and are resilient to sensor failures or measurement depletion. To this end, rather than the standard VINS paradigm using a minimal sensing suite of a single camera and IMU, in this paper we design a real-time consistent multi-IMU multi-camera (MIMC)-VINS estimator that is able to seamlessly fuse multi-modal …
The vast majority of visual animals actively control their eyes, heads, and/or bodies to direct their gaze toward different parts of their environment. In contrast, recent applications of reinforcement learning in robotic manipulation employ cameras as passive sensors. These are carefully placed to view a scene from a fixed pose. Active perception allows animals to gather the most relevant information about the world and focus their computational resources where needed. It also enables them to view objects from different distances and viewpoints, providing a rich visual experience from which t…
ECHO: Event-Augmented Context with Hindsight and Outlook for Wrist-Only Manipulation
The Hong Kong University of Science and Technology（香港科技大学）；The University of British Columbia（不列颠哥伦比亚大学）；MMLab, The Chinese University of Hong Kong（香港中文大学多媒体实验室）；Astribot；ZENBOT
Institute for Infocomm Research (IR), A*STAR（A*STAR资讯通信研究院（IR））；Institute for High Performance Computing (IHPC), A*STAR（A*STAR高性能计算研究院（IHPC））
We present KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system. We train the deep neural network only in the simulated environment; and the trained model could be directly used for real-world visual servoing tasks. KOVIS consists of two networks. The first keypoint network learns the keypoint representation from the image using with an autoencoder. Then the visual servoing network learns the motion based on keypoints extracted from the camera image. The two networks are trained end-to-end in the simula…
Precision robotic manipulation tasks (insertion, screwing, precisely pick, precisely place) are required in many scenarios. Previous methods achieved good performance on such manipulation tasks. However, such methods typically require tedious calibration or expensive sensors. 3D/RGB-D cameras and torque/force sensors add to the cost of the robotic application and may not always be economical. In this work, we aim to solve these but using only weak-calibrated and low-cost webcams. We propose Binocular Alignment Learning (BAL), which could automatically learn the eye-hand coordination and points…
R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation
Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Key Laboratory of Embodied Intelligence Systems（北京智能体智能系统重点实验室）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学智能体与机器人研究院）；GigaAI（极佳科技）
Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulation
Sun Yat-sen University（中山大学）；Pengcheng Laboratory（鹏城实验室）；Nanyang Technological University（南洋理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；X-Era AI Lab（X-Era AI实验室）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Stuttgart（斯图加特大学）
In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation. Existing methods are limited by calibrated cameras and off-line processing. Thus, we present the first method (AirPose) to estimate human pose and shape using images captured by multiple extrinsically uncalibrated flying cameras. AirPose itself calibrates the cameras relative to the person instead of relying on any pre-calibration. It uses distributed neural networks …
This paper presents two visual trackers from the different paradigms of learning and registration based tracking and evaluates their application in image based visual servoing. They can track object motion with four degrees of freedom (DoF) which, as we will show here, is sufficient for many fine manipulation tasks. One of these trackers is a newly developed learning based tracker that relies on learning discriminative correlation filters while the other is a refinement of a recent 8 DoF RANSAC based tracker adapted with a new appearance model for tracking 4 DoF motion. Both trackers are shown…
AnyViewDex: View-Invariant Dexterous Manipulation from RGB Observations
Robotics Research Center, IIIT-H（IIIT-H机器人研究中心）；Veermata Jijabai Technological Institute Mumbai（孟买维尔马塔·吉贾拜技术学院）；Indian Institute of Science Education and Research Bhopal（印度科学教育与研究学院博帕尔分校）
Carnegie Mellon University（卡内基梅隆大学）；Apple Inc.（苹果公司）；The University of Adelaide（阿德莱德大学）
Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences given known calibration and sufficient views. However in practice, expensive multi-view setups -- involving tens sometimes hundreds of cameras -- are required in order to obtain the high fidelity 3D reconstructions necessary for many modern applications. In this paper we present a novel approach that leverages recent advances in 2D-3D lifting using neural shape priors while also enforcing multi-view equivariance. We show how our method can achieve comparable fidelity to expensive calibrated multi-view r…
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device. Our system can perform general manipulation tasks such as cloth folding, hammering and 3mm clearance peg in hole. We propose the use of non-Cartesian oblique coordinate frame, dynamic motion scaling and reposition of operator frames to increase the flexibility of our teleoperation system. We hypothesize that lowering the barrier of entry to teleoperation will allow for wider deployment of supervised autonomy system, which will in turn generates realistic datasets that unlock the potential of m…
SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（信息多媒体国家重点实验室，计算机科学学院，北京大学）；School of Software, Beihang University（软件学院，北航）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Multi-Robot Planning and Control from CCTV Camera Networks in a Real Warehouse
Oxford Robotics Institute, University of Oxford, UK（牛津大学机器人研究所，牛津大学，英国）；Robot Systems Group, Technical University of Munich, Germany（机器人系统组，慕尼黑技术大学，德国）
This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementation of the original environment. Besides, we provide users with new APIs to access a joint control mode, image observations and goals with customisable camera and a built-in on-hand camera. We further design a set of multi-step, multi-goal, long-hori…
Viewpoint Matters: Dynamically Optimizing Viewpoints with Masked Autoencoder for Visual Manipulation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；the School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Rutgers University（罗格斯大学）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
