51 research outputs found
Learning Manipulation under Physics Constraints with Visual Perception
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure
Review of the techniques used in motorâcognitive humanârobot skill transfer
Abstract A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of generalâpurpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to generalâpurpose manipulators or mobile robots to replicate humanâlike behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's lowâlevel motor and highâlevel decisionâmaking ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous highâlevel decisionâmaking ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested
Robot Arm Pose Estimation through PixelâWise Part Classification
We propose to frame the problem of marker-less robot arm pose estimation as a pixel-wise part classification problem. As input, we use a depth image in which each pixel is classified to be either from a particular robot part or the background. The classifier is a random decision forest trained on a large number of synthetically generated and labeled depth images. From all the training samples ending up at a leaf node, a set of offsets is learned that votes for relative joint positions. Pooling these votes over all foreground pixels and subsequent clustering gives us an estimate of the true joint positions. Due to the intrinsic parallelism of pixel-wise classification, this approach can run in super real-time and is more efficient than previous ICP-like methods. We quantitatively evaluate the accuracy of this approach on synthetic data. We also demonstrate that the method produces accurate joint estimates on real data despite being purely trained on synthetic data
Interaction of electrons with lattice vibrations in ammonium chloride and ammonium bromide
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