Dynamics, control and sensor issues pertinent to robotic hands for the EVA retriever system

Basic dynamics, sensor, control, and related artificial intelligence issues pertinent to smart robotic hands for the Extra Vehicular Activity (EVA) Retriever system are summarized and discussed. These smart hands are to be used as end effectors on arms attached to manned maneuvering units (MMU). The Retriever robotic systems comprised of MMU, arm and smart hands, are being developed to aid crewmen in the performance of routine EVA tasks including tool and object retrieval. The ultimate goal is to enhance the effectiveness of EVA crewmen

Overcoming Exploration in Reinforcement Learning with Demonstrations

Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and manually shaping a reward function can result in suboptimal performance. However, finding a non-zero reward is exponentially more difficult with increasing task horizon or action dimensionality. This puts many real-world tasks out of practical reach of RL methods. In this work, we use demonstrations to overcome the exploration problem and successfully learn to perform long-horizon, multi-step robotics tasks with continuous control such as stacking blocks with a robot arm. Our method, which builds on top of Deep Deterministic Policy Gradients and Hindsight Experience Replay, provides an order of magnitude of speedup over RL on simulated robotics tasks. It is simple to implement and makes only the additional assumption that we can collect a small set of demonstrations. Furthermore, our method is able to solve tasks not solvable by either RL or behavior cloning alone, and often ends up outperforming the demonstrator policy.Comment: 8 pages, ICRA 201

The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects

To operate safely and efficiently alongside human workers, collaborative robots (cobots) require the ability to quickly understand the dynamics of manipulated objects. However, traditional methods for estimating the full set of inertial parameters rely on motions that are necessarily fast and unsafe (to achieve a sufficient signal-to-noise ratio). In this work, we take an alternative approach: by combining visual and force-torque measurements, we develop an inertial parameter identification algorithm that requires slow or 'stop-and-go' motions only, and hence is ideally tailored for use around humans. Our technique, called Homogeneous Part Segmentation (HPS), leverages the observation that man-made objects are often composed of distinct, homogeneous parts. We combine a surface-based point clustering method with a volumetric shape segmentation algorithm to quickly produce a part-level segmentation of a manipulated object; the segmented representation is then used by HPS to accurately estimate the object's inertial parameters. To benchmark our algorithm, we create and utilize a novel dataset consisting of realistic meshes, segmented point clouds, and inertial parameters for 20 common workshop tools. Finally, we demonstrate the real-world performance and accuracy of HPS by performing an intricate 'hammer balancing act' autonomously and online with a low-cost collaborative robotic arm. Our code and dataset are open source and freely available.Comment: Accepted to the IEEE International Conference on Robotics and Automation (ICRA'23), London, UK, May 29 - June 2, 202

Vision-based Robotic Grasping in Simulation using Deep Reinforcement Learning

This thesis will investigate different robotic manipulation and grasping approaches. It will present an overview of robotic simulation environments, and offer an evaluation of PyBullet, CoppeliaSim, and Gazebo, comparing various features. The thesis further presents a background for current approaches to robotic manipulation and grasping by describing how the robotic movement and grasping can be organized. State-of-the-Art approaches for learning robotic grasping, both using supervised methods and reinforcement learning methods are presented. Two set of experiments will be conducted in PyBullet, illustrating how Deep Reinforcement Learning methods could be applied to train a 7 degrees of freedom robotic arm to grasp objects