Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations Although a large portion of deep RL research ha

pdf/how-to-train-your-robot-with-deep-reinforcement-learning-40dhk148qv.pdf

How to train your robot with deep reinforcement learning: lessons we have learned:

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time,real world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn; as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned.

Dexterous robotic hands are appealing for their agility and human-like morphology, yet their high degree of freedom makes learning to manipulate challenging. We introduce an approach for learning dexterous grasping. Our key idea is to embed an object-centric visual affordance model within a deep reinforcement learning loop to learn grasping policies that favor the same object regions favored by people. Unlike traditional approaches that learn from human demonstration trajectories (e.g., hand joint sequences captured with a glove), the proposed prior is object-centric and image-based, allowing the agent to anticipate useful affordance regions for objects unseen during policy learning. We demonstrate our idea with a 30-DoF five-fingered robotic hand simulator on 40 objects from two datasets, where it successfully and efficiently learns policies for stable functional grasps. Our affordance-guided policies are significantly more effective, generalize better to novel objects, train 3× faster than the baselines, and are more robust to noisy sensor readings and actuation. Our work offers a step towards manipulation agents that learn by watching how people use objects, without requiring state and action information about the human body. Project website with videos: http://vision.cs.utexas.edu/projects/graff-dexterous-affordance-grasp.

Learning Dexterous Grasping with Object-Centric Visual Affordances

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

/pdf/object-detection-recognition-and-robot-grasping-based-on-3omlo05jkl.pdf

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Wearable computing platforms, such as smartwatches and head-mounted mixed reality displays, demand new input devices for high-fidelity interaction. We present AuraRing, a wearable magnetic tracking system designed for tracking fine-grained finger movement. The hardware consists of a ring with an embedded electromagnetic transmitter coil and a wristband with multiple sensor coils. By measuring the magnetic fields at different points around the wrist, AuraRing estimates the five degree-of-freedom pose of the ring. We develop two different approaches to pose reconstruction---a first-principles iterative approach and a closed-form neural network approach. Notably, AuraRing requires no runtime supervised training, ensuring user and session independence. AuraRing has a resolution of 0.1 mm and a dynamic accuracy of 4.4 mm, as measured through a user evaluation with optical ground truth. The ring is completely self-contained and consumes just 2.3 mW of power.

AuraRing: Precise Electromagnetic Finger Tracking

Input is a significant problem for wearable systems, particularly for head mounted virtual and augmented reality displays. Existing input techniques either lack expressive power or may not be socially acceptable. As an alternative, thumb-to-finger touches present a promising input mechanism that is subtle yet capable of complex interactions. We present DigiTouch, a reconfigurable glove-based input device that enables thumb-to-finger touch interaction by sensing continuous touch position and pressure. Our novel sensing technique improves the reliability of continuous touch tracking and estimating pressure on resistive fabric interfaces. We demonstrate DigiTouch’s utility by enabling a set of easily reachable and reconfigurable widgets such as buttons and sliders. Since DigiTouch senses continuous touch position, widget layouts can be customized according to user preferences and application needs. As an example of a real-world application of this reconfigurable input device, we examine a split-QWERTY keyboard layout mapped to the user’s fingers. We evaluate DigiTouch for text entry using a multi-session study. With our continuous sensing method, users reliably learned to type and achieved a mean typing speed of 16.0 words per minute at the end of ten 20-minute sessions, an improvement over similar wearable touch systems.

DigiTouch: Reconfigurable Thumb-to-Finger Input and Text Entry on Head-mounted Displays

Multi-fingered dexterous hands are versatile and capable of acquiring a diverse set of skills such as grasping, in-hand manipulation, and tool use. To fully utilize their versatility in real-world scenarios, we require algorithms and policies that can control them using on-board sensing capabilities, without relying on external tracking or motion capture systems. Cameras and tactile sensors are the most widely used on-board sensors that do not require instrumentation of the world. In this work, we demonstrate an imitation learning based approach to train deep visuomotor policies for a variety of manipulation tasks with a simulated five fingered dexterous hand. These policies directly control the hand using high dimensional visual observations of the world and propreoceptive observations from the robot, and can be trained efficiently with a few hundred expert demonstration trajectories. We also find that using touch sensing information enables faster learning and better asymptotic performance for tasks with high degree of occlusions. Video demonstration of our results are available at: https://sites.google.com/view/hand-vil/

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

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DigiTouch: Reconfigurable Thumb-to-Finger Input and Text Entry on Head-mounted Displays

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation